Instructions to use slenk/codewraith-lora-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use slenk/codewraith-lora-8b with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/llama-3.1-8b-instruct-unsloth-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "slenk/codewraith-lora-8b")

Transformers

How to use slenk/codewraith-lora-8b with Transformers:

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

pipe = pipeline("text-generation", model="slenk/codewraith-lora-8b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("slenk/codewraith-lora-8b", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use slenk/codewraith-lora-8b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "slenk/codewraith-lora-8b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "slenk/codewraith-lora-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/slenk/codewraith-lora-8b

SGLang

How to use slenk/codewraith-lora-8b 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 "slenk/codewraith-lora-8b" \
    --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": "slenk/codewraith-lora-8b",
		"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 "slenk/codewraith-lora-8b" \
        --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": "slenk/codewraith-lora-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use slenk/codewraith-lora-8b with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for slenk/codewraith-lora-8b to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for slenk/codewraith-lora-8b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for slenk/codewraith-lora-8b to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="slenk/codewraith-lora-8b",
    max_seq_length=2048,
)

Docker Model Runner
How to use slenk/codewraith-lora-8b with Docker Model Runner:
```
docker model run hf.co/slenk/codewraith-lora-8b
```

slenk commited on Apr 13

Commit

c8aa8a9

verified ·

1 Parent(s): 3b0fc42

v5: Qwen2.5-Coder 32B teacher, 0.99 structural score

Browse files

Files changed (3) hide show

README.md +2 -2
adapter_config.json +5 -5
adapter_model.safetensors +1 -1

README.md CHANGED Viewed

@@ -1,7 +1,7 @@
 ---
 base_model: unsloth/llama-3.1-8b-instruct-unsloth-bnb-4bit
 library_name: peft
-model_name: codewraith-lora-8b-v4
 tags:
 - base_model:adapter:unsloth/llama-3.1-8b-instruct-unsloth-bnb-4bit
 - lora
@@ -13,7 +13,7 @@ licence: license
 pipeline_tag: text-generation
 ---
-# Model Card for codewraith-lora-8b-v4
 This model is a fine-tuned version of [unsloth/llama-3.1-8b-instruct-unsloth-bnb-4bit](https://huggingface.co/unsloth/llama-3.1-8b-instruct-unsloth-bnb-4bit).
 It has been trained using [TRL](https://github.com/huggingface/trl).

 ---
 base_model: unsloth/llama-3.1-8b-instruct-unsloth-bnb-4bit
 library_name: peft
+model_name: codewraith-lora-8b-v5
 tags:
 - base_model:adapter:unsloth/llama-3.1-8b-instruct-unsloth-bnb-4bit
 - lora
 pipeline_tag: text-generation
 ---
+# Model Card for codewraith-lora-8b-v5
 This model is a fine-tuned version of [unsloth/llama-3.1-8b-instruct-unsloth-bnb-4bit](https://huggingface.co/unsloth/llama-3.1-8b-instruct-unsloth-bnb-4bit).
 It has been trained using [TRL](https://github.com/huggingface/trl).

adapter_config.json CHANGED Viewed

@@ -33,13 +33,13 @@
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   "task_type": "CAUSAL_LM",

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   "revision": null,
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+    "q_proj",
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     "down_proj",
+    "up_proj"
   ],
   "target_parameters": null,
   "task_type": "CAUSAL_LM",

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