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mattshumer_Reflection-Llama-3.1-70B-8.0bpw-h8-exl2

Text Generation
Transformers
Safetensors
llama
conversational
text-generation-inference
Inference Endpoints
8-bit precision
exl2
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  1. README.md +62 -0
  2. config.json +50 -0
  3. generation_config.json +12 -0
  4. huggingface-metadata.txt +166 -0
  5. measurement.json +0 -0
  6. model.safetensors.index.json +730 -0
  7. output-00001-of-00008.safetensors +3 -0
  8. output-00002-of-00008.safetensors +3 -0
  9. output-00003-of-00008.safetensors +3 -0
  10. output-00004-of-00008.safetensors +3 -0
  11. output-00005-of-00008.safetensors +3 -0
  12. output-00006-of-00008.safetensors +3 -0
  13. output-00007-of-00008.safetensors +3 -0
  14. output-00008-of-00008.safetensors +3 -0
  15. special_tokens_map.json +61 -0
  16. tokenizer.json +0 -0
  17. tokenizer_config.json +2119 -0
README.md ADDED
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+ ---
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+ license: llama3.1
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+ base_model: meta-llama/Meta-Llama-3.1-70B-Instruct
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+ pipeline_tag: text-generation
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+ ---
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+ # Reflection Llama-3.1 70B
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+
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+ **Reflection 70B is (currently) the world's top open-source LLM, trained with a new technique called Reflection-Tuning that teaches a LLM to detect mistakes in its reasoning and correct course.**
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+
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+ The model was trained on synthetic data generated by [Glaive](https://glaive.ai). If you're training a model, Glaive is incredible — use them.
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+
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+ You can [try the model here](https://reflection-playground-production.up.railway.app/).
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+
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+ ## Benchmarks
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/60518f3731c5be7f3dd5ebc3/zNs-ZFs0SbnomH7mikiOU.png)
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+
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+ All benchmarks tested have been checked for contamination by running [LMSys's LLM Decontaminator](https://github.com/lm-sys/llm-decontaminator). When benchmarking, we isolate the `<output>` and benchmark on solely that section.
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+
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+ Trained from Llama 3.1 70B Instruct, you can sample from Reflection 70B using the same code, pipelines, etc. as any other Llama model. It even uses the stock Llama 3.1 chat template format (though, we've trained in a few new special tokens to aid in reasoning and reflection).
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+
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+ During sampling, the model will start by outputting reasoning inside `<thinking>` and `</thinking>` tags, and then once it is satisfied with its reasoning, it will output the final answer inside `<output>` and `</output>` tags. Each of these tags are special tokens, trained into the model.
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+
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+ This enables the model to separate its internal thoughts and reasoning from its final answer, improving the experience for the user.
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+
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+ Inside the `<thinking>` section, the model may output one or more `<reflection>` tags, which signals the model has caught an error in its reasoning and will attempt to correct it before providing a final answer.
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+
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+ ## System Prompt
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+
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+ ```
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+ The system prompt used for training this model is:
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+
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+ You are a world-class AI system, capable of complex reasoning and reflection. Reason through the query inside <thinking> tags, and then provide your final response inside <output> tags. If you detect that you made a mistake in your reasoning at any point, correct yourself inside <reflection> tags.
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+
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+ We recommend using this exact system prompt to get the best results from Reflection 70B. You may also want to experiment combining this system prompt with your own custom instructions to customize the behavior of the model.
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+ ```
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+
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+ ## Chat Format
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+
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+ As mentioned above, the model uses the standard Llama 3.1 chat format. Here’s an example:
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+
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+ ```
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+ <|begin_of_text|><|start_header_id|>system<|end_header_id|>
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+
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+ You are a world-class AI system, capable of complex reasoning and reflection. Reason through the query inside <thinking> tags, and then provide your final response inside <output> tags. If you detect that you made a mistake in your reasoning at any point, correct yourself inside <reflection> tags.<|eot_id|><|start_header_id|>user<|end_header_id|>
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+
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+ what is 2+2?<|eot_id|><|start_header_id|>assistant<|end_header_id|>
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+ ```
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+
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+ ## Tips for Performance
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+
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+ - We are initially recommending a `temperature` of `.7` and a `top_p` of `.95`.
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+ - For increased accuracy, append `Think carefully.` at the end of your messages.
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+
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+ ## Dataset / Report
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+
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+ Both the dataset and a brief report detailing how we trained this model will be released next week, alongside our Reflection 405B model that we expect will be the top-performing LLM in the world, including closed-source models.
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+
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+ ---
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+
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+ Thanks to Jason Kuperberg and Josh Bickett from the [HyperWrite](https://hyperwriteai.com) team for reviewing drafts of the report we'll be releasing next week.
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+
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+ Also, we know right now the model is split into a ton of files. We'll condense this soon to make the model easier to download and work with!
config.json ADDED
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+ {
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+ "_name_or_path": "meta-llama/Meta-Llama-3.1-70B-Instruct",
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+ "architectures": [
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+ "LlamaForCausalLM"
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+ ],
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+ "attention_bias": false,
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+ "attention_dropout": 0.0,
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+ "bos_token_id": 128000,
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+ "eos_token_id": [
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+ 128001,
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+ ],
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+ "hidden_act": "silu",
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+ "hidden_size": 8192,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 28672,
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+ "max_position_embeddings": 131072,
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+ "mlp_bias": false,
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+ "model_type": "llama",
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+ "num_attention_heads": 64,
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+ "num_hidden_layers": 80,
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+ "num_key_value_heads": 8,
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+ "pretraining_tp": 1,
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+ "rms_norm_eps": 1e-05,
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+ "rope_scaling": {
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+ "factor": 8.0,
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+ "high_freq_factor": 4.0,
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+ "low_freq_factor": 1.0,
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+ "original_max_position_embeddings": 8192,
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+ "rope_type": "llama3"
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+ },
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+ "rope_theta": 500000.0,
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+ "tie_word_embeddings": false,
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.44.0",
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+ "use_cache": true,
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+ "vocab_size": 128256,
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+ "quantization_config": {
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+ "quant_method": "exl2",
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+ "version": "0.1.9",
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+ "bits": 8.0,
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+ "head_bits": 8,
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+ "calibration": {
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+ "rows": 115,
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+ "length": 2048,
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+ "dataset": "(default)"
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+ }
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+ }
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+ }
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+ {
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+ "bos_token_id": 128000,
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+ "do_sample": true,
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+ "temperature": 0.7,
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+ "top_p": 0.95,
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+ "transformers_version": "4.44.0"
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+ }
huggingface-metadata.txt ADDED
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+ url: https://huggingface.co/mattshumer/Reflection-70B
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+ branch: main
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+ download date: 2024-09-05 21:02:56
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+ "bos_token": "<|begin_of_text|>",
2109
+ "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n {%- set date_string = \"26 Jul 2024\" %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n{%- else %}\n {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message + builtin tools #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if builtin_tools is defined or tools is not none %}\n {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{%- if builtin_tools is defined %}\n {{- \"Tools: \" + builtin_tools | reject('equalto', 'code_interpreter') | join(\", \") + \"\\n\\n\"}}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n {#- Extract the first user message so we can plug it in here #}\n {%- if messages | length != 0 %}\n {%- set first_user_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n {%- else %}\n {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n {%- elif 'tool_calls' in message %}\n {%- if not message.tool_calls|length == 1 %}\n {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n {%- endif %}\n {%- set tool_call = message.tool_calls[0].function %}\n {%- if builtin_tools is defined and tool_call.name in builtin_tools %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- \"<|python_tag|>\" + tool_call.name + \".call(\" }}\n {%- for arg_name, arg_val in tool_call.arguments | items %}\n {{- arg_name + '=\"' + arg_val + '\"' }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \")\" }}\n {%- else %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n {{- '\"parameters\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- \"}\" }}\n {%- endif %}\n {%- if builtin_tools is defined %}\n {#- This means we're in ipython mode #}\n {{- \"<|eom_id|>\" }}\n {%- else %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n {%- if message.content is mapping or message.content is iterable %}\n {{- message.content | tojson }}\n {%- else %}\n {{- message.content }}\n {%- endif %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n",
2110
+ "clean_up_tokenization_spaces": true,
2111
+ "eos_token": "<|eot_id|>",
2112
+ "model_input_names": [
2113
+ "input_ids",
2114
+ "attention_mask"
2115
+ ],
2116
+ "model_max_length": 131072,
2117
+ "pad_token": "<|eot_id|>",
2118
+ "tokenizer_class": "PreTrainedTokenizerFast"
2119
+ }