Text Generation
Transformers
Safetensors
mistral
Merge
mergekit
Intel/neural-chat-7b-v3-3
cognitivecomputations/samantha-mistral-7b
Eval Results (legacy)
text-generation-inference
How to use from
SGLangUse Docker images
docker run --gpus all \
--shm-size 32g \
-p 30000:30000 \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HF_TOKEN=<secret>" \
--ipc=host \
lmsysorg/sglang:latest \
python3 -m sglang.launch_server \
--model-path "fhai50032/SamChat" \
--host 0.0.0.0 \
--port 30000# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "fhai50032/SamChat",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Quick Links
SamChat
SamChat is a merge of the following models
🧩 Configuration
models:
- model: macadeliccc/WestLake-7B-v2-laser-truthy-dpo
# no params for base model
- model: Intel/neural-chat-7b-v3-3
parameters:
weight: 0.55
density: 0.46
- model: cognitivecomputations/samantha-mistral-7b
parameters:
weight: 0.64
density: 0.55
merge_method: dare_ties
base_model: macadeliccc/WestLake-7B-v2-laser-truthy-dpo
parameters:
normalize: true
int8_mask: true
dtype: float16
💻 Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "fhai50032/SamChat"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 61.68 |
| AI2 Reasoning Challenge (25-Shot) | 62.20 |
| HellaSwag (10-Shot) | 81.88 |
| MMLU (5-Shot) | 59.70 |
| TruthfulQA (0-shot) | 52.89 |
| Winogrande (5-shot) | 72.14 |
| GSM8k (5-shot) | 41.24 |
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Model tree for fhai50032/SamChat
Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard62.200
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard81.880
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard59.700
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard52.890
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard72.140
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard41.240
Install from pip and serve model
# Install SGLang from pip: pip install sglang# Start the SGLang server: python3 -m sglang.launch_server \ --model-path "fhai50032/SamChat" \ --host 0.0.0.0 \ --port 30000# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "fhai50032/SamChat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'