erfanzar
/

Raven-v0.1

@@ -1,201 +1,123 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 ---
 library_name: transformers
+datasets:
+- erfanzar/MoD-Prompts
+- erfanzar/GPT-4-Prompts
+language:
+- en
+pipeline_tag: text-generation
 ---
+# Raven Fine-Tuned Gemma-2B
+Raven is a Fine-tuned version of google/gemma-2 whith same prompting style of gemma-2b-it which trained Using TPU VM v4-64 and [EasyDeL](https://github.com/erfanzar/EasyDeL)
+both fine-tuning and serving code are available and it's recommended to use JAX-EasyDeL Gemma since HF-Gemma implementaion is Wrong.
+### Serving and Using Raven
+```python
+from EasyDel import JAXServer, JAXServerConfig, EasyServe
+from fjformer import get_dtype
+from EasyDel.serve.prompters import GemmaPrompter, Llama2Prompter, OpenChatPrompter, ChatMLPrompter
+from EasyDel.serve.prompters.base_prompter import BasePrompter
+from jax import numpy as jnp, lax
+import jax
+from typing import List, Union, Optional
+max_sequence_length = 8192
+max_compile_tokens = 256
+max_new_tokens_ratio = 25
+dtype = "fp16"
+prompter_type = "gemma"
+sharding_axis_dims = (1, 1, 1, -1)
+pretrained_model_name_or_path = "erfanzar/Raven-v0.1"
+attn_mechanism = "normal"
+scan_mlp_chunk_size = max_compile_tokens
+use_scan_mlp = True
+scan_ring_attention = True
+block_k = 128
+block_q = 128
+use_sharded_kv_caching = False
+server_config = JAXServerConfig(
+    max_sequence_length=max_sequence_length,
+    max_compile_tokens=max_compile_tokens,
+    max_new_tokens=max_compile_tokens * max_new_tokens_ratio,
+    dtype=dtype,
+    pre_compile=False,
+    eos_token_id=107
+)
+prompters = {
+    "gemma": GemmaPrompter(),
+    "llama": Llama2Prompter(),
+    "openchat": OpenChatPrompter(),
+    "chatml": ChatMLPrompter()
+}
+prompter: BasePrompter = prompters[prompter_type]
+class JAXServerC(JAXServer):
+    @staticmethod
+    def format_chat(history: List[List[str]], prompt: str, system: Union[str, None]) -> str:
+        return prompter.format_message(
+            history=history,
+            prompt=prompt,
+            system_message=system,
+            prefix=None
+        )
+    @staticmethod
+    def format_instruct(system: str, instruction: str) -> str:
+        return prompter.format_message(
+            prefix=None,
+            system_message=system,
+            prompt=instruction,
+            history=[]
+        )
+server = JAXServerC.from_torch_pretrained(
+    server_config=server_config,
+    pretrained_model_name_or_path=pretrained_model_name_or_path,
+    device=jax.devices('cpu')[0],
+    dtype=get_dtype(dtype=dtype),
+    param_dtype=get_dtype(dtype=dtype),
+    precision=jax.lax.Precision("fastest"),
+    sharding_axis_dims=sharding_axis_dims,
+    sharding_axis_names=("dp", "fsdp", "tp", "sp"),
+    input_shape=(1, server_config.max_sequence_length),
+    model_config_kwargs=dict(
+        fully_sharded_data_parallel=True,
+        attn_mechanism=attn_mechanism,
+        scan_mlp_chunk_size=max_compile_tokens,
+        use_scan_mlp=use_scan_mlp,
+        scan_ring_attention=scan_ring_attention,
+        block_k=block_k,
+        block_q=block_q,
+        use_sharded_kv_caching=use_sharded_kv_caching
+    )
+)
+history = []
+while True:
+    user_prompt = input("> ")
+    model_prompt = server.format_chat(
+        history,
+        user_prompt,
+        "You are an AI assistant be respect-full and explain detailed questions step by step."
+    )
+    past_response_length = 0
+    for response, used_tokens in server.sample(
+        model_prompt,
+        greedy=False
+    ):
+        print(response[past_response_length:], end="")
+        past_response_length = len(response)
+    history.append([user_prompt, response])
+```
+Gradio UI is also available via `server.gradio_inference().launch()`.