Instructions to use junaidali/qwenadapters with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use junaidali/qwenadapters with Transformers:

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

pipe = pipeline("text-generation", model="junaidali/qwenadapters")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("junaidali/qwenadapters")
model = AutoModelForMultimodalLM.from_pretrained("junaidali/qwenadapters")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

MLX

How to use junaidali/qwenadapters with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("junaidali/qwenadapters")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

vLLM

How to use junaidali/qwenadapters with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/junaidali/qwenadapters

SGLang

How to use junaidali/qwenadapters 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 "junaidali/qwenadapters" \
    --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": "junaidali/qwenadapters",
		"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 "junaidali/qwenadapters" \
        --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": "junaidali/qwenadapters",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

How to use junaidali/qwenadapters with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "junaidali/qwenadapters"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "junaidali/qwenadapters"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use junaidali/qwenadapters with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "junaidali/qwenadapters"

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default junaidali/qwenadapters

Run Hermes

hermes

OpenClaw new

How to use junaidali/qwenadapters with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "junaidali/qwenadapters"

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "junaidali/qwenadapters" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

MLX LM

How to use junaidali/qwenadapters with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "junaidali/qwenadapters"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "junaidali/qwenadapters"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "junaidali/qwenadapters",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use junaidali/qwenadapters with Docker Model Runner:
```
docker model run hf.co/junaidali/qwenadapters
```

Qwen-AgentWorld-35B-A3B — v130 CPA merged

This is Qwen/Qwen-AgentWorld-35B-A3B with the v130 CPA LoRA adapter merged into the weights (full bf16, ready to load with transformers).

Provenance

Base model: Qwen/Qwen-AgentWorld-35B-A3B (Qwen3.5-MoE, 35B total / A3B active, bf16)
Adapter: qwen-cpa-v130-pretrain-fix — a rank-16 LoRA trained with MLX-LM (scale=32, dropout=0.05, 3300 iters, lr 1e-6), covering the top 12 decoder layers (28–39).

The adapter was trained in the MLX format and converted to PEFT before merging (adapter_config.json uses r=16, lora_alpha=512 so that scaling = alpha/r = 32 matches the MLX multiplier).

Merged modules

Deltas were folded in at fp32 precision, then cast back to bf16:

Group	Modules
Full-attention layers (31, 35, 39)	`self_attn.{q,k,v,o}_proj`
Linear-attention layers	`linear_attn.{in_proj_a,in_proj_b,in_proj_qkv,in_proj_z,out_proj}`
MoE routing / shared expert (all 12 layers)	`mlp.gate`, `mlp.shared_expert.{gate,up,down}_proj`, `mlp.shared_expert_gate`
Fused routed experts (all 12 layers)	`mlp.experts.gate_up_proj`, `mlp.experts.down_proj`

For a plain nn.Linear the delta is scale · (lora_a @ lora_b)ᵀ; for the fused per-expert MoE weights it is scale · bmm(lora_b, lora_a), with the gate_proj/up_proj halves written into the two row-blocks of the fused gate_up_proj.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("junaidali/qwenadapters")
model = AutoModelForCausalLM.from_pretrained(
    "junaidali/qwenadapters", torch_dtype="bfloat16", device_map="auto",
    trust_remote_code=True)

Policy note from the training manifest: v130 CPA SFT candidate — benchmark before deploy.

Downloads last month: 825

Safetensors

Model size

35B params

Tensor type

BF16

MLX

Hardware compatibility

Quantized

Model tree for junaidali/qwenadapters

Base model

Qwen/Qwen3.5-35B-A3B-Base

Finetuned

Qwen/Qwen-AgentWorld-35B-A3B

Adapter

(2)

this model