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

Libraries

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

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

pipe = pipeline("text-generation", model="solidrust/Mewthree-7B-AWQ")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("solidrust/Mewthree-7B-AWQ")
model = AutoModelForMultimodalLM.from_pretrained("solidrust/Mewthree-7B-AWQ")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "solidrust/Mewthree-7B-AWQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "solidrust/Mewthree-7B-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use solidrust/Mewthree-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/Mewthree-7B-AWQ" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "solidrust/Mewthree-7B-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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/Mewthree-7B-AWQ" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "solidrust/Mewthree-7B-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

Suparious commited on Mar 7, 2024

Commit

b9c1697

verified ·

1 Parent(s): 0291fd5

Add initial model card

Browse files

Files changed (1) hide show

README.md +52 -1

README.md CHANGED Viewed

@@ -1,3 +1,54 @@
 ---
-license: apache-2.0
 ---

 ---
+language:
+- en
+base_model:
+- Krisbiantoro/mistral7b_dpo_en
+library_name: transformers
+tags:
+  - mergekit
+  - merge
+  - quantized
+  - 4-bit
+  - AWQ
+  - transformers
+  - pytorch
+  - mistral
+  - text-generation
+  - conversational
+  - autotrain_compatible
+  - endpoints_compatible
+  - text-generation-inference
+  - chatml
+license: other
+model_creator: jeiku
+model_name: Mewthree-7B
+model_type: mistral
+pipeline_tag: text-generation
+inference: false
+prompt_template: '<|im_start|>system
+  {system_message}<|im_end|>
+  <|im_start|>user
+  {prompt}<|im_end|>
+  <|im_start|>assistant
+  '
+quantized_by: Suparious
 ---
+# jeiku/Mewthree-7B AWQ
+- Model creator: [jeiku](https://huggingface.co/jeiku)
+- Original model: [Mewthree-7B](https://huggingface.co/jeiku/Mewthree_7B)
+![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/626dfb8786671a29c715f8a9/mfFtubKCh143741_enqN8.jpeg)
+## Model Summary
+Draws upon the Prodigy lineage with some no robots tossed in for good measure. Dipped its toes in some memerboard essence and added a kiss of BioMistral for anatomy. Applied a DPO LoRA over top.
+Seems to do markdown well. It's an overall balanced model with a focus on RP.