Instructions to use solidrust/dolphin-2.8-mistral-7b-v02-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use solidrust/dolphin-2.8-mistral-7b-v02-AWQ with Transformers:

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

pipe = pipeline("text-generation", model="solidrust/dolphin-2.8-mistral-7b-v02-AWQ")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("solidrust/dolphin-2.8-mistral-7b-v02-AWQ")
model = AutoModelForCausalLM.from_pretrained("solidrust/dolphin-2.8-mistral-7b-v02-AWQ")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use solidrust/dolphin-2.8-mistral-7b-v02-AWQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "solidrust/dolphin-2.8-mistral-7b-v02-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/dolphin-2.8-mistral-7b-v02-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/solidrust/dolphin-2.8-mistral-7b-v02-AWQ

SGLang

How to use solidrust/dolphin-2.8-mistral-7b-v02-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/dolphin-2.8-mistral-7b-v02-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/dolphin-2.8-mistral-7b-v02-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/dolphin-2.8-mistral-7b-v02-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/dolphin-2.8-mistral-7b-v02-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use solidrust/dolphin-2.8-mistral-7b-v02-AWQ with Docker Model Runner:
```
docker model run hf.co/solidrust/dolphin-2.8-mistral-7b-v02-AWQ
```

Performed my own Quantization, now encounter an error while running inference with vllm.

by ryan-rozanitis-bd - opened Jun 5, 2025

Discussion

ryan-rozanitis-bd

Jun 5, 2025

Hi solidrust! Firstly, we are big fans of your quantized model! We have been using it for a few months.

Now, we want to iterate upon it using the process below.

Finetune the base model. This results in a new model.safetensors.
Merge this model.safetensor back into the base model.safetensors, by loading the finetuned safetensor state dict to the model.
Quantize the result of step 2.

When running trying to use this model with vllm, I get an error like below. Did you encounter this when you quantized the base model?
RuntimeError: start (0) + length (7168) exceeds dimension size (4096).

Suparious

SolidRusT Networks org Jun 11, 2025

I don't remember, but there are usually two ways to solve that error:

update the config.json, then quantize the model again (recommeneded)
find a related setting or flag in vllm to override dimention size.

Suparious changed discussion status to closed Jun 11, 2025

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