Helion-4x34B
This is the model for Helion-4x34B. I used this repo to make this MOE model.
Prompt Template(s):
Since bagel-dpo-34b-v0.2 uses many prompt templates, you can utilize prompt templates provided by bagel and other expert's prompt templates.
Note: I currently do not know which prompt template is best.
ChatML:
<|im_start|>system
{system}<|im_end|>
<|im_start|>user
{user}<|im_end|>
<|im_start|>assistant
{asistant}<|im_end|>
Human Asistant
Human: {user}
### Assistant: {asistant}
Alpaca (sort of)
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{system}
{instruction}
### Response:
Vicuna
{system}
USER: {instruction}
ASSISTANT:
Visit bagel-dpo-34b-v0.2 to try more prompt templates.
Yaml Config to reproduce
base_model: nontoxic-bagel-34b-v0.2
gate_mode: hidden
dtype: bfloat16
experts:
- source_model: bagel-dpo-34b-v0.2
positive_prompts: ["question answering", "Q:", science", "biology", "chemistry", "physics"]
negative_prompts: ["math", "reason", "mathematics", "solve", "count", "code", "python", "javascript", "programming", "algorithm"]
- source_model: Nous-Hermes-2-Yi-34B
positive_prompts: ["chat", "math", "reason", "mathematics", "solve", "count", "python", "javascript", "programming", "algorithm", "tell me", "assistant"]
- source_model: SUS-Chat-34B
positive_prompts: ["math", "reason", "mathematics", "solve", "count", "assistant"]
- source_model: platypus-yi-34b
positive_prompts: [""]
negative_prompts: ["math", "reason", "mathematics", "solve", "count"]
Quantizationed versions
Quantizationed versions of this model is available thanks to TheBloke.
GPTQ
GGUF
AWQ
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 75.48 |
| AI2 Reasoning Challenge (25-Shot) | 69.71 |
| HellaSwag (10-Shot) | 85.28 |
| MMLU (5-Shot) | 77.33 |
| TruthfulQA (0-shot) | 63.91 |
| Winogrande (5-shot) | 84.37 |
| GSM8k (5-shot) | 72.25 |
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