Instructions to use solidrust/flammen23X-mistral-7B-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use solidrust/flammen23X-mistral-7B-AWQ with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="solidrust/flammen23X-mistral-7B-AWQ")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("solidrust/flammen23X-mistral-7B-AWQ")
model = AutoModelForCausalLM.from_pretrained("solidrust/flammen23X-mistral-7B-AWQ", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use solidrust/flammen23X-mistral-7B-AWQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "solidrust/flammen23X-mistral-7B-AWQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "solidrust/flammen23X-mistral-7B-AWQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/solidrust/flammen23X-mistral-7B-AWQ

SGLang

How to use solidrust/flammen23X-mistral-7B-AWQ with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "solidrust/flammen23X-mistral-7B-AWQ" \
    --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": "solidrust/flammen23X-mistral-7B-AWQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use 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 "solidrust/flammen23X-mistral-7B-AWQ" \
        --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": "solidrust/flammen23X-mistral-7B-AWQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use solidrust/flammen23X-mistral-7B-AWQ with Docker Model Runner:
```
docker model run hf.co/solidrust/flammen23X-mistral-7B-AWQ
```

Suparious commited on Sep 3, 2024

Commit

ee80fc6

verified ·

1 Parent(s): 37d5a92

Updated and moved existing to merged_models base_model tag in README.md

Browse files

Files changed (1) hide show

README.md +7 -6

README.md CHANGED Viewed

@@ -1,19 +1,20 @@
 ---
 library_name: transformers
 license: apache-2.0
-base_model:
 - flammenai/flammen23-mistral-7B
-datasets:
-- flammenai/character-roleplay-DPO
 tags:
 - 4-bit
 - AWQ
 - text-generation
 - autotrain_compatible
 - endpoints_compatible
-pipeline_tag: text-generation
-inference: false
-quantized_by: Suparious
 ---
 # flammenai/flammen23X-mistral-7B AWQ

 ---
+base_model: flammenai/flammen23X-mistral-7B
+datasets:
+- flammenai/character-roleplay-DPO
+inference: false
 library_name: transformers
 license: apache-2.0
+merged_models:
 - flammenai/flammen23-mistral-7B
+pipeline_tag: text-generation
+quantized_by: Suparious
 tags:
 - 4-bit
 - AWQ
 - text-generation
 - autotrain_compatible
 - endpoints_compatible
 ---
 # flammenai/flammen23X-mistral-7B AWQ