Instructions to use mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless with Transformers:

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

pipe = pipeline("feature-extraction", model="mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless")
model = AutoModel.from_pretrained("mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless", device_map="auto")
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 Settings

llama.cpp

How to use mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0
# Run inference directly in the terminal:
llama cli -hf mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0
# Run inference directly in the terminal:
llama cli -hf mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0

Use Docker

docker model run hf.co/mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0

LM Studio
Jan
Ollama
How to use mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless with Ollama:
```
ollama run hf.co/mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0
```
Unsloth Desktop

How to use mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless with Docker Model Runner:
```
docker model run hf.co/mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0
```

Lemonade

How to use mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0

Run and chat with the model

lemonade run user.qwen3.5-4l-vocab40k-en-ko-headless-Q8_0

List all available models

lemonade list

Hermes Agent

How to use mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0

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 mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0

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 "mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

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

qwen3.5-4l-vocab40k-en-ko-headless / config.json

mp-juuuns

Replace the root with the task-blind 32k backbone; add figures, the full benchmark record and the ported runtime

c99961f verified 4 days ago

Raw

History Blame Contribute Delete

2.48 kB

	{
	"architectures": [
	"Qwen3_5TextModel"
	],
	"attention_bias": false,
	"attention_dropout": 0.0,
	"attn_output_gate": true,
	"bos_token_id": null,
	"dtype": "bfloat16",
	"eos_token_id": 39835,
	"full_attention_interval": 4,
	"head_dim": 256,
	"hidden_act": "silu",
	"hidden_size": 1024,
	"initializer_range": 0.02,
	"intermediate_size": 3584,
	"layer_types": [
	"linear_attention",
	"full_attention",
	"linear_attention",
	"full_attention"
	],
	"linear_conv_kernel_dim": 4,
	"linear_key_head_dim": 128,
	"linear_num_key_heads": 16,
	"linear_num_value_heads": 16,
	"linear_value_head_dim": 128,
	"mamba_ssm_dtype": "float32",
	"max_position_embeddings": 262144,
	"mlp_only_layers": [],
	"model_type": "qwen3_5_text",
	"mtp_num_hidden_layers": 1,
	"mtp_use_dedicated_embeddings": false,
	"num_attention_heads": 8,
	"num_hidden_layers": 4,
	"num_key_value_heads": 2,
	"pad_token_id": 39833,
	"partial_rotary_factor": 0.25,
	"rms_norm_eps": 1e-06,
	"rope_parameters": {
	"mrope_interleaved": true,
	"mrope_section": [
	11,
	11,
	10
	],
	"partial_rotary_factor": 0.25,
	"rope_theta": 10000000,
	"rope_type": "default"
	},
	"standalone4l_distillation": {
	"schema_version": "qwen35-stage-map-v1",
	"selected_layers": [
	0,
	2,
	3,
	5
	],
	"selection_basis": "semeval_ranked_inherited",
	"selection_basis_note": "the layer map was selected by ranking candidates on SemEval-2020 Task 11 labeled data (scripts/probe_qwen35_student4l_from6l_v6.py records phase_v6_map_selection_split 'public_gold_train', rank 1). It is inherited here and disclosed; no task-blind justification is claimed for it.",
	"source_depth": 6,
	"stage": "6to4",
	"target_depth": 4,
	"task_labels_used_for_weight_materialization": false
	},
	"standalone4l_role": "task_agnostic_classification_base_4l",
	"tie_word_embeddings": true,
	"transformers_version": "5.13.0",
	"use_cache": false,
	"vocab_size": 39866,
	"standalone4l_base": {
	"schema_version": "standalone4l-task-agnostic-base-v1",
	"role": "classification_ready_headless_text_backbone",
	"task_labels_used_in_base_training": false,
	"semeval_used_in_base_training": false,
	"tokenizer": "task_blind_cut_N32768_of_upstream_full_qwen35",
	"training_corpus_rows": 4096,
	"training_objective": "hidden_and_interface_representation_distillation",
	"published_classification_head": false
	}
	}