Instructions to use Ashapu/Anarva-Coder-Qwen3-8B-algorithm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ashapu/Anarva-Coder-Qwen3-8B-algorithm with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/qwen3-8b-unsloth-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "Ashapu/Anarva-Coder-Qwen3-8B-algorithm")

Transformers

How to use Ashapu/Anarva-Coder-Qwen3-8B-algorithm with Transformers:

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

pipe = pipeline("text-generation", model="Ashapu/Anarva-Coder-Qwen3-8B-algorithm")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Ashapu/Anarva-Coder-Qwen3-8B-algorithm", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Ashapu/Anarva-Coder-Qwen3-8B-algorithm with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ashapu/Anarva-Coder-Qwen3-8B-algorithm"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ashapu/Anarva-Coder-Qwen3-8B-algorithm",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Ashapu/Anarva-Coder-Qwen3-8B-algorithm

SGLang

How to use Ashapu/Anarva-Coder-Qwen3-8B-algorithm 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 "Ashapu/Anarva-Coder-Qwen3-8B-algorithm" \
    --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": "Ashapu/Anarva-Coder-Qwen3-8B-algorithm",
		"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 "Ashapu/Anarva-Coder-Qwen3-8B-algorithm" \
        --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": "Ashapu/Anarva-Coder-Qwen3-8B-algorithm",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use Ashapu/Anarva-Coder-Qwen3-8B-algorithm with Docker Model Runner:
```
docker model run hf.co/Ashapu/Anarva-Coder-Qwen3-8B-algorithm
```

Ashapu commited on about 1 month ago

Commit

94ed9d0

verified ·

1 Parent(s): a8f98e4

ATF v1 — algorithm checkpoint

Browse files

Files changed (2) hide show

adapter_config.json +5 -5
atf_metadata.json +1 -1

adapter_config.json CHANGED Viewed

@@ -34,13 +34,13 @@
   "rank_pattern": {},
   "revision": null,
   "target_modules": [
-    "gate_proj",
-    "down_proj",
     "up_proj",
-    "o_proj",
-    "v_proj",
     "q_proj",
-    "k_proj"
   ],
   "target_parameters": null,
   "task_type": "CAUSAL_LM",

   "rank_pattern": {},
   "revision": null,
   "target_modules": [
     "up_proj",
     "q_proj",
+    "down_proj",
+    "v_proj",
+    "k_proj",
+    "o_proj",
+    "gate_proj"
   ],
   "target_parameters": null,
   "task_type": "CAUSAL_LM",

atf_metadata.json CHANGED Viewed

@@ -1,5 +1,5 @@
 {
   "stage": "algorithm",
-  "saved_at": "2026-07-29T13:21:28.969999",
   "type": "adapter_only"
 }

 {
   "stage": "algorithm",
+  "saved_at": "2026-07-31T09:47:24.368641",
   "type": "adapter_only"
 }