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---
license: other
license_name: yi-license
license_link: https://huggingface.co/01-ai/Yi-34B/blob/main/LICENSE
language:
- en
library_name: transformers
base_model: []
tags:
- mergekit
- merge
- Yi
- exllama
- exllamav2
- exl2
---
## Warning
This quant is cursed and most likely not 4.65bpw, even though I ran the standard script to do the quant. Still investigating into it. It does work, though! Apologies for inconvenience.

---
# RPMerge
A merge of several Yi 34B models with a singular goal: 40K+ context, instruct-enhanced storytelling.

Disappointed with some quirks of my previous kitchen sink merges (like token/instruct formats from various models showing up when they shouldn't), I've gone 'back to the basics' and picked a few Vicuna-format only models:

- [DrNicefellow/ChatAllInOne-Yi-34B-200K-V1](https://huggingface.co/DrNicefellow/ChatAllInOne-Yi-34B-200K-V1) and [migtissera/Tess-34B-v1.5b](https://huggingface.co/migtissera/Tess-34B-v1.5b) both have excellent general instruction-following performance.

- [cgato/Thespis-34b-v0.7](https://huggingface.co/cgato/Thespis-34b-v0.7) is trained on the "Username: {Input} / BotName: {Response}" format, to emphasize it in the merge (but not force it). It also seems to work for multi-character stories.

- [Doctor-Shotgun/limarpv3-yi-llama-34b-lora](https://huggingface.co/Doctor-Shotgun/limarpv3-yi-llama-34b-lora) is trained on roleplaying data, but merged at a modest weight to not over emphasize it. This is the only non-vicuna model (being alpaca format), but it doesn't seem to interefere with the Vicuna format or adversely affect long-context perplexity

- [adamo1139/yi-34b-200k-rawrr-dpo-2](https://huggingface.co/adamo1139/yi-34b-200k-rawrr-dpo-2) the base for the limarp lora, this is base Yi gently finetuned to discourage refusals.

- [migtissera/Tess-M-Creative-v1.0](https://huggingface.co/migtissera/Tess-M-Creative-v1.0) and [NousResearch/Nous-Capybara-34B](https://huggingface.co/NousResearch/Nous-Capybara-34B) are both "undertrained" Yi models. I find they excel at raw completion performance (like long novel continuations) while still retaining some Vicuna instruct ability. This may be why some still prefer the original Tess 1.0/Capybara merge.

I consider this a more "focused" merge that previous ones. I will investigate other models (perhaps chatML models?) for a more "factual assistant" focused merge, as well as a coding-focused merge if I can't find one to suit my needs.


## Prompt template: Orca-Vicuna
```
SYSTEM: {system_message}
USER: {prompt}
ASSISTANT:
```
Raw prompting as described here is also effective: https://old.reddit.com/r/LocalLLaMA/comments/18zqy4s/the_secret_to_writing_quality_stories_with_llms/

As well as a very explicit system prompt like this: https://old.reddit.com/r/LocalLLaMA/comments/1aiz6zu/roleplaying_system_prompts/koygiwa/


## Running

Chinese models with large tokenizer vocabularies like Yi need *careful* parameter tuning due to their huge logit sampling "tails." Yi in particular also runs relatively "hot" even at lower temperatures.

I am a huge fan of Kalomaze's quadratic sampling (shown as "smoothing factor" where available), as described here: https://github.com/oobabooga/text-generation-webui/pull/5403

Otherwise, I recommend a lower temperature with 0.1 or higher MinP, a little repetition penalty, and mirostat with a low tau, and no other samplers. See the explanation here: https://github.com/ggerganov/llama.cpp/pull/3841

@MarinaraSpaghetti has extensively tested the model and recommended the following settings. They seem to work quite well:

```
{
    "temp": 1,
    "temperature_last": true,
    "top_p": 1,
    "top_k": 0,
    "top_a": 0,
    "tfs": 1,
    "epsilon_cutoff": 0,
    "eta_cutoff": 0,
    "typical_p": 0.9,
    "min_p": 0,
    "rep_pen": 1.1,
    "rep_pen_range": 19456,
    "no_repeat_ngram_size": 0,
    "penalty_alpha": 0,
    "num_beams": 1,
    "length_penalty": 0,
    "min_length": 0,
    "encoder_rep_pen": 1,
    "freq_pen": 0,
    "presence_pen": 0,
    "do_sample": true,
    "early_stopping": false,
    "dynatemp": false,
    "min_temp": 1,
    "max_temp": 2,
    "dynatemp_exponent": 1,
    "smoothing_factor": 0.33,
    "add_bos_token": false,
    "truncation_length": 2048,
    "ban_eos_token": false,
    "skip_special_tokens": true,
    "streaming": true,
    "mirostat_mode": 0,
    "mirostat_tau": 5,
    "mirostat_eta": 0.1,
    "guidance_scale": 1,
    "negative_prompt": "",
    "grammar_string": "",
    "banned_tokens": "",
    "ignore_eos_token_aphrodite": false,
    "spaces_between_special_tokens_aphrodite": true,
    "sampler_order": [
        6,
        0,
        1,
        3,
        4,
        2,
        5
    ],
    "logit_bias": [],
    "n": 1,
    "rep_pen_size": 0,
    "genamt": 400,
    "max_length": 38912
}
```

24GB GPUs can efficiently run Yi-34B-200K models at **40K-90K context** with exllamav2, and performant UIs like [exui](https://github.com/turboderp/exui). I go into more detail in this [post](https://old.reddit.com/r/LocalLLaMA/comments/1896igc/how_i_run_34b_models_at_75k_context_on_24gb_fast/). Empty 16GB GPUs can still run the high context with aggressive quantization.

To load/train this in full-context backends like transformers, you *must* change `max_position_embeddings` in config.json to a lower value than 200,000, otherwise you will OOM! I do not recommend running high context without context-efficient backends that support flash attention + 8 bit kv cache, like exllamav2, litellm, vllm or unsloth.


## Testing Notes

Thanks to ParasiticRogue for this idea of a Vicuna-only merge, see: https://huggingface.co/brucethemoose/jondurbin_bagel-dpo-34b-v0.2-exl2-4bpw-fiction/discussions

See: https://huggingface.co/brucethemoose/Yi-34B-200K-DARE-megamerge-v8#testing-notes

This is a possible base for a storytelling finetune/LASER in the future, once I can bite the bullet and rent some A100s or a MI300. 

I have tested this merge with with novel-style continuation (but not much chat-style roleplay), and some assistant-style responses and long context analysis. I haven't seen any refusals so far.

## Merge Details
### Merge Method

This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using /home/alpha/Models/Raw/chargoddard_Yi-34B-200K-Llama as a base.

### Models Merged

The following models were included in the merge:
* /home/alpha/Models/Raw/migtissera_Tess-34B-v1.5b
* /home/alpha/Models/Raw/migtissera_Tess-M-Creative-v1.0
* /home/alpha/Models/Raw/cgato_Thespis-34b-DPO-v0.7
* /home/alpha/Models/Raw/Nous-Capybara-34B
* /home/alpha/Models/Raw/admo_limarp
* /home/alpha/Models/Raw/DrNicefellow_ChatAllInOne-Yi-34B-200K-V1

### Configuration

The following YAML configuration was used to produce this model:

```yaml
models:
  - model: /home/alpha/Models/Raw/chargoddard_Yi-34B-200K-Llama
    # No parameters necessary for base model
  - model: /home/alpha/Models/Raw/migtissera_Tess-34B-v1.5b
    #Emphasize the beginning of Vicuna format models
    parameters:
      weight: 0.19
      density: 0.59
  - model: /home/alpha/Models/Raw/Nous-Capybara-34B
    parameters:
      weight: 0.19
      density: 0.55
  # Vicuna format
  - model: /home/alpha/Models/Raw/migtissera_Tess-M-Creative-v1.0
    parameters:
      weight: 0.05
      density: 0.55
  - model: /home/alpha/Models/Raw/DrNicefellow_ChatAllInOne-Yi-34B-200K-V1
    parameters:
      weight: 0.19
      density: 0.55
  - model: adamo1139/yi-34b-200k-rawrr-dpo-2+Doctor-Shotgun/limarpv3-yi-llama-34b-lora
    parameters:
      weight: 0.19
      density: 0.48
  - model: /home/alpha/Models/Raw/cgato_Thespis-34b-DPO-v0.7
    parameters:
      weight: 0.19
      density: 0.59


merge_method: dare_ties
tokenizer_source: union
base_model: /home/alpha/Models/Raw/chargoddard_Yi-34B-200K-Llama
parameters:
  int8_mask: true
dtype: bfloat16
```


## Self Promotion

I'm part of a AI startup called Holocene AI!

We're new, busy, and still setting things up. But if you have any business inquiries, want a job, or just want some consultation, feel free to shoot me an email. We have expertise in RAG applications and llama/embeddings model finetuning, and absolutely *none* of the nonsense of scammy AI startups.

Contact me at: [email protected]

I also set up a Ko-Fi! I want to run some (personal) training/LASERing as well, at 100K context or so. If you'd like to buy me 10 minutes on an A100 (or 5 seconds on an MI300X), I'd appreciate it: https://ko-fi.com/alphaatlas