Instructions to use lmstudio-community/Seed-OSS-36B-Instruct-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lmstudio-community/Seed-OSS-36B-Instruct-MLX-4bit with Transformers:

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

pipe = pipeline("text-generation", model="lmstudio-community/Seed-OSS-36B-Instruct-MLX-4bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("lmstudio-community/Seed-OSS-36B-Instruct-MLX-4bit")
model = AutoModelForCausalLM.from_pretrained("lmstudio-community/Seed-OSS-36B-Instruct-MLX-4bit", 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]:]))

MLX

How to use lmstudio-community/Seed-OSS-36B-Instruct-MLX-4bit 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("lmstudio-community/Seed-OSS-36B-Instruct-MLX-4bit")

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 lmstudio-community/Seed-OSS-36B-Instruct-MLX-4bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lmstudio-community/Seed-OSS-36B-Instruct-MLX-4bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lmstudio-community/Seed-OSS-36B-Instruct-MLX-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/lmstudio-community/Seed-OSS-36B-Instruct-MLX-4bit

SGLang

How to use lmstudio-community/Seed-OSS-36B-Instruct-MLX-4bit 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 "lmstudio-community/Seed-OSS-36B-Instruct-MLX-4bit" \
    --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": "lmstudio-community/Seed-OSS-36B-Instruct-MLX-4bit",
		"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 "lmstudio-community/Seed-OSS-36B-Instruct-MLX-4bit" \
        --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": "lmstudio-community/Seed-OSS-36B-Instruct-MLX-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

How to use lmstudio-community/Seed-OSS-36B-Instruct-MLX-4bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "lmstudio-community/Seed-OSS-36B-Instruct-MLX-4bit"

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": "lmstudio-community/Seed-OSS-36B-Instruct-MLX-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use lmstudio-community/Seed-OSS-36B-Instruct-MLX-4bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "lmstudio-community/Seed-OSS-36B-Instruct-MLX-4bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "lmstudio-community/Seed-OSS-36B-Instruct-MLX-4bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "lmstudio-community/Seed-OSS-36B-Instruct-MLX-4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use lmstudio-community/Seed-OSS-36B-Instruct-MLX-4bit with Docker Model Runner:
```
docker model run hf.co/lmstudio-community/Seed-OSS-36B-Instruct-MLX-4bit
```

Hermes Agent

How to use lmstudio-community/Seed-OSS-36B-Instruct-MLX-4bit 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 "lmstudio-community/Seed-OSS-36B-Instruct-MLX-4bit"

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 lmstudio-community/Seed-OSS-36B-Instruct-MLX-4bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use lmstudio-community/Seed-OSS-36B-Instruct-MLX-4bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "lmstudio-community/Seed-OSS-36B-Instruct-MLX-4bit"

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 "lmstudio-community/Seed-OSS-36B-Instruct-MLX-4bit" \
  --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"

neilmehta24 commited on Aug 26, 2025

Commit

aedfa6b

verified ·

1 Parent(s): 52733b2

Update chat_template.jinja

Browse files

Files changed (1) hide show

chat_template.jinja +9 -9

chat_template.jinja CHANGED Viewed

@@ -23,18 +23,18 @@
         {%- set ns.interval = budget_values_v05[i] -%}
     {%- endif -%}
 {%- endfor -%}
-{# 若超过最大档位，则用最后一个档位的值 #}
 {%- if ns.interval is none -%}
     {%- set ns.interval = budget_values_v05[-1] -%}
 {%- endif -%}
-{# ---------- 预处理 system 消息 ---------- #}
 {%- if messages[0]["role"] == "system" %}
 {%- set system_message = messages[0]["content"] %}
 {%- set loop_messages = messages[1:] %}
 {%- else %}
 {%- set loop_messages = messages %}
 {%- endif %}
-{# ---------- 确保 tools 存在 ---------- #}
 {%- if not tools is defined or tools is none %}
 {%- set tools = [] %}
 {%- endif %}
@@ -46,7 +46,7 @@
     {%- elif t == "array" -%}list
     {%- else -%}Any{%- endif -%}
 {%- endmacro -%}
-{# ---------- 输出 system 块 ---------- #}
 {%- if system_message is defined %}
 {{ bos_token + "system\n" + system_message }}
 {%- else %}
@@ -100,7 +100,7 @@ def {{ item.function.name }}(
 {{"工具调用请遵循如下格式:\n<seed:tool_call>\n<function=example_function_name>\n<parameter=example_parameter_1>value_1</parameter>\n<parameter=example_parameter_2>This is the value for the second parameter\nthat can span\nmultiple lines</parameter>\n</function>\n</seed:tool_call>\n"}}
 {%- endif %}
-{# 结束 system 块行尾 #}
 {%- if system_message is defined or tools is iterable and tools | length > 0 %}
 {{ eos_token }}
 {%- endif %}
@@ -116,7 +116,7 @@ def {{ item.function.name }}(
 {{ eos_token }}
 {%- endif %}
 {%- endif %}
-{# ---------- 逐条写出历史消息 ---------- #}
 {%- for message in loop_messages %}
 {%- if message.role == "assistant"
   and message.tool_calls is defined
@@ -152,15 +152,15 @@ def {{ item.function.name }}(
 {%- if message.content is defined and message.content is string and message.content | trim | length > 0 %}
 {{ "\n" + message.content | trim + eos_token }}
 {%- endif %}
-{# 包括 tool 角色，在这个逻辑 #}
 {%- else %}
 {{ bos_token + message.role + "\n" + message.content + eos_token }}
 {%- endif %}
 {%- endfor %}
-{# ---------- 控制模型开始续写 ---------- #}
 {%- if add_generation_prompt %}
 {{ bos_token+"assistant\n" }}
 {%- if thinking_budget == 0 %}
-{{ think_begin_token+budget_begin_token }}
 {%- endif %}
 {%- endif %}

         {%- set ns.interval = budget_values_v05[i] -%}
     {%- endif -%}
 {%- endfor -%}
+{# Find the first gear that is greater than or equal to the thinking_budget. #}
 {%- if ns.interval is none -%}
     {%- set ns.interval = budget_values_v05[-1] -%}
 {%- endif -%}
+{# ---------- Preprocess the system message ---------- #}
 {%- if messages[0]["role"] == "system" %}
 {%- set system_message = messages[0]["content"] %}
 {%- set loop_messages = messages[1:] %}
 {%- else %}
 {%- set loop_messages = messages %}
 {%- endif %}
+{# ---------- Ensure tools exist ---------- #}
 {%- if not tools is defined or tools is none %}
 {%- set tools = [] %}
 {%- endif %}
     {%- elif t == "array" -%}list
     {%- else -%}Any{%- endif -%}
 {%- endmacro -%}
+{# ---------- Output the system block ---------- #}
 {%- if system_message is defined %}
 {{ bos_token + "system\n" + system_message }}
 {%- else %}
 {{"工具调用请遵循如下格式:\n<seed:tool_call>\n<function=example_function_name>\n<parameter=example_parameter_1>value_1</parameter>\n<parameter=example_parameter_2>This is the value for the second parameter\nthat can span\nmultiple lines</parameter>\n</function>\n</seed:tool_call>\n"}}
 {%- endif %}
+{# End the system block line #}
 {%- if system_message is defined or tools is iterable and tools | length > 0 %}
 {{ eos_token }}
 {%- endif %}
 {{ eos_token }}
 {%- endif %}
 {%- endif %}
+{# ---------- List the historical messages one by one ---------- #}
 {%- for message in loop_messages %}
 {%- if message.role == "assistant"
   and message.tool_calls is defined
 {%- if message.content is defined and message.content is string and message.content | trim | length > 0 %}
 {{ "\n" + message.content | trim + eos_token }}
 {%- endif %}
+{# Include the tool role #}
 {%- else %}
 {{ bos_token + message.role + "\n" + message.content + eos_token }}
 {%- endif %}
 {%- endfor %}
+{# ---------- Control the model to start continuation ---------- #}
 {%- if add_generation_prompt %}
 {{ bos_token+"assistant\n" }}
 {%- if thinking_budget == 0 %}
+{{ think_begin_token + "\n" + budget_begin_token + "The current thinking budget is 0, so I will directly start answering the question." + budget_end_token + "\n" + think_end_token }}
 {%- endif %}
 {%- endif %}