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deepseek-coder-6.7b-chat - GGUF
- Model creator: https://huggingface.co/AIGym/
- Original model: https://huggingface.co/AIGym/deepseek-coder-6.7b-chat/
Original model description:
license: apache-2.0 tags: - finetuned pipeline_tag: text-generation model-index: - name: deepseek-coder-6.7b-chat results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 36.01 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AIGym/deepseek-coder-6.7b-chat name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 53.74 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AIGym/deepseek-coder-6.7b-chat name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 38.22 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AIGym/deepseek-coder-6.7b-chat name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 42.94 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AIGym/deepseek-coder-6.7b-chat name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 57.54 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AIGym/deepseek-coder-6.7b-chat name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 16.98 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AIGym/deepseek-coder-6.7b-chat name: Open LLM Leaderboard
deepseek-coder-6.7B-chat
It was created by starting with the deepseek-coder-6.7B and training it on the open assistant dataset. We have attached the wandb report in pdf form to view the training run at a glance.
Reson
This model was fine tned to allow it to follow direction and is a steeping stone to further training, but still would be good for asking qestions about code.
How to use
You will need the transformers>=4.31
from transformers import AutoTokenizer
import transformers
import torch
model = "AIGym/deepseek-coder-6.7b-chat"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
prompt = "What are the values in open source projects?"
formatted_prompt = (
f"### Human: {prompt}### Assistant:"
)
sequences = pipeline(
formatted_prompt,
do_sample=True,
top_k=50,
top_p = 0.7,
num_return_sequences=1,
repetition_penalty=1.1,
max_new_tokens=500,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
Referrals
Run Pod - This is who I use to train th emodels on huggingface. If you use it we both get free crdits. - Visit Runpod's Website!
Paypal - If you want to leave a tip, it is appecaheted. - Visit My Paypal!
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 40.90 |
| AI2 Reasoning Challenge (25-Shot) | 36.01 |
| HellaSwag (10-Shot) | 53.74 |
| MMLU (5-Shot) | 38.22 |
| TruthfulQA (0-shot) | 42.94 |
| Winogrande (5-shot) | 57.54 |
| GSM8k (5-shot) | 16.98 |
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