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+---
+base_model:
+- CohereLabs/North-Mini-Code-1.0
+inference: false
+library_name: transformers
+license: apache-2.0
+tags:
+- conversational
+- unsloth
+- chat
+- code
+- agent
+---
+<div>
+<p style="margin-top: 0;margin-bottom: 0;">
+    <em><a href="https://docs.unsloth.ai/basics/unsloth-dynamic-v2.0-gguf">Unsloth Dynamic 2.0</a> achieves superior accuracy & outperforms other leading quants.</em>
+  </p>
+  <div style="display: flex; gap: 5px; align-items: center; ">
+    <a href="https://github.com/unslothai/unsloth/">
+      <img src="https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png" width="133">
+    </a>
+    <a href="https://discord.gg/unsloth">
+      <img src="https://github.com/unslothai/unsloth/raw/main/images/Discord%20button.png" width="173">
+    </a>
+    <a href="https://docs.unsloth.ai/">
+      <img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="143">
+    </a>
+  </div>
+</div>
+
+
+# **Model Card for North Mini Code**
+
+## **Model Summary**
+
+North Mini Code is an open weights research release of a 30B-A3B parameter model optimized for code generation, agentic software engineering, and terminal tasks.
+
+Developed by: [Cohere](https://cohere.com/) and [Cohere Labs](https://cohere.com/research)
+
+* Point of Contact: [**Cohere Labs**](https://cohere.com/research)  
+* License: Apache 2.0
+* Model: North Mini Code  
+* Model Size: 30B total; 3B active  
+* Context length: 256K & 64K max output
+
+For more details about this model, please check out our [blog post](https://huggingface.co/blog/CohereLabs/introducing-north-mini-code). 
+
+**Try North Mini Code**
+
+You can try out North Mini Code before downloading the weights in OpenCode and our hosted [Hugging Face Space](https://huggingface.co/spaces/CohereLabs/North-Mini-Code-1.0).
+
+**Evaluation**
+
+![image1](https://cdn-uploads.huggingface.co/production/uploads/62668f725fb8d521d94d8451/xR7kZ3X9RKEZrbgD6hpG1.png)
+
+<details>
+<summary><span style="font-size: 80%;"><b>Benchmarking Methodology [CLICK TO EXPAND]</span></b></summary>
+
+- <span style="font-size: 80%;">We used SWE-Bench Verified, SWE-Bench Pro, Terminal-Bench v2, and Terminal-Bench Hard to benchmark North Mini Code's agentic coding capabilities. For evaluation harnesses, we used the Swe-Agent harness v1.1.0 for SWE-Bench, and a simple ReAct harness employing a single terminal-use tool based on Harbor's Tmux session implementation for Terminal-Bench v2. For Terminal Bench Hard, we directly used Terminus-2, following the same methodology as the Artificial Analysis Intelligence Index to compare North-Mini-Code-1.0 with the other models. Additionally, we used SciCode and LiveCodeBench v6 as complex code-generation benchmarks outside of tool use.</span>
+- <span style="font-size: 80%;">We run each benchmark with 3 different seeds and report the average benchmark performance, using temperature=1.0 and top\_p=0.95. We used publicly reported scores for competitor models, either from original reports or the Artificial Analysis Intelligence Index, where available. Additionally, Gemma4’s scores for agentic coding tasks were reported by [Qwen team](https://qwen.ai/blog?id=qwen3.6-35b-a3b). For benchmark results that any public report is missing, denoted by (\*) in the figure, we run them internally using the recommended model configuration.</span>
+</details>
+
+**Usage**
+
+Please install transformers from the source repository that includes the necessary changes for this model. We recommend using the following set of sampling parameters for generation: \`temperature=1.0\`, \`top\_p=0.95\`.
+
+```py
+# pip install transformers
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_id = "CohereLabs/North-Mini-Code-1.0"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+
+prompt = "Write a python program to check if a string is a palindrome or not."
+
+# Format message with the North-Mini-Code-1.0 chat template
+messages = [{"role": "user", "content": prompt}]
+input_ids = tokenizer.apply_chat_template(
+    messages,
+    tokenize=True,
+    add_generation_prompt=True,
+    return_tensors="pt",
+)
+
+gen_tokens = model.generate(
+    **input_ids, 
+    max_new_tokens=1024, 
+    do_sample=True, 
+    temperature=1.0,
+    top_p=0.95
+)
+
+gen_text = tokenizer.decode(gen_tokens[0])
+print(gen_text)
+```
+
+You can also use the model directly using transformers `pipeline` abstraction:
+
+```py
+from transformers import pipeline
+import torch
+
+model_id = "CohereLabs/North-Mini-Code-1.0"
+
+prompt = """Given a list of unique words each of size k and an n sized word, w, where n is a multiple of k,
+Write a program in python to determine the number of unique combinations of words in the list that can be concatenated to form an anagram of the word w.
+"""
+
+pipe = pipeline(
+    "text-generation",
+    model=model_id,
+    torch_dtype="auto",
+    device_map="auto",
+)
+
+messages = [
+    {"role": "user", "content": f"{prompt}"},
+]
+
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+)
+
+
+outputs = pipe(
+    messages,
+    max_new_tokens=1024,
+    do_sample=True, 
+    temperature=1.0,
+    top_p=0.95
+
+)
+
+print(outputs[0]["generated_text"][-1])
+
+```
+
+## **Model Details**
+
+**Input**: Text only.
+
+**Output**: Model generates text. 
+
+**Model Architecture**: North-Mini-Code-1.0 is a decoder-only Transformer-based sparse Mixture-of-Experts model. It uses an efficient attention implementation, interleaved between sliding-window attention with RoPE and global attention with no positional embeddings, in a 3:1 ratio. The feed-forward block is an MoE block with 128 experts, of which 8 are activated per token. Each expert block is an FFN block with SwiGLU activation. The router applies a sigmoid activation function to the logits before the top-k selection. We also use a single dense layer before the sparse layers. North-Mini-Code-1.0 was post-trained using a two-stage cascaded supervised fine-tuning (SFT) followed by reinforcement learning with verifiable rewards (RLVR), focusing on agentic coding.  For more technical details, please check out our [blog post](https://huggingface.co/blog/CohereLabs/introducing-north-mini-code). 
+
+**Context Length:** North-Mini-Code-1.0 supports a context length of 256K & 64K output length.
+
+### **Tool Use Capabilities:**
+
+North-Mini-Code-1.0 has been specifically trained with tool-use capabilities for agentic coding.
+
+Tool use with North-Mini-Code-1.0 is supported through [chat templates](https://huggingface.co/docs/transformers/main/en/chat_templating#advanced-tool-use--function-calling) in Transformers. We recommend providing tool descriptions using JSON schema.
+
+**Tool Use Example \[CLICK TO EXPAND\]** 
+
+```py
+# Define tools
+tools = [{
+  "type": "function",
+  "function": {
+    "name": "bash",
+    "description": "Execute a bash command in the terminal.",
+    "parameters": {
+      "type": "object",
+      "properties": {
+        "command": {
+          "description": "The bash command to execute.",
+          "type": "string"
+        }
+      },
+      "required": ["command"]
+    },
+  }
+}]
+
+# Define conversation input
+conversation = [{"role": "user", "content": "Find out if there is any json file in this folder"}]
+
+
+# Get the Tool Use prompt
+input_prompt = tokenizer.apply_chat_template(conversation=conversation, tools=tools, tokenize=False, add_generation_prompt=True, return_tensors="pt")
+
+# Tokenize the prompt
+input_ids = tokenizer(input_prompt, return_tensors="pt")
+```
+
+You can then generate from this input as normal.
+
+North Mini Code, similarly as all the other Cohere agent models released to date, supports [interleaved thinking](https://docs.vllm.ai/en/latest/features/interleaved_thinking/) and works best when turned on. You’re strongly encouraged to pass on all the model-generated thinking contents to future agentic steps, and chat turns for the best model performance. Please refer to the linked vllm doc and see how it’s done.
+
+If the model generates thinking content and tool calls, you should add both of them to the chat history like so:
+
+```py
+# Pass on the tool_call and thinking
+tool_call = {"name": "bash", "arguments": {"command": "ls -al"}}
+reasoning = "The user wants to find if there are any JSON files in the current folder. I should use the `ls` command to list files and then check if there are any JSON files (files ending with .json). Let me first list the files in the current directory."
+
+conversation.append({"role": "assistant", "tool_calls": [{"id": "0", "type": "function", "function": tool_call}], "reasoning": reasoning})
+```
+
+and then call the tool and append the result, as a dictionary, with the tool role, like so:
+
+```py
+# This needs to be a dictionary
+tool_result = {"stdout": "test.json\ntest.py", "return_code": "0"} 
+
+# Append tool results
+conversation.append({"role": "tool", "tool_call_id": "0", "content": tool_result})
+```
+
+After that, you can `generate()` again to let the model use the tool result in the chat.
+
+Note that this was a very brief introduction to tool calling \- for more information the Transformers [tool use documentation](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling).
+
+### **vLLM**
+
+You can also run the model in vLLM. Please use vLLM main for North Mini Code until a new release is available, and accurate response parsing also requires installing Cohere’s melody library.
+
+```shell
+uv pip install "git+https://github.com/vllm-project/vllm.git"
+uv pip install cohere_melody>=0.9.0
+```
+
+Then the vllm server can be started with the following command:
+
+```shell
+vllm serve CohereLabs/North-Mini-Code-1.0 \
+  -tp 2 \
+  --max-model-len 320000 \
+  --tool-call-parser cohere_command4 \
+  --reasoning-parser cohere_command4 \
+  --enable-auto-tool-choice
+```
+
+**Use locally deployed North Mini Code in OpenCode:**
+
+Please use OpenCode main branch until a new release is available.
+
+```shell
+# Example commands to install on linux
+git clone https://github.com/anomalyco/opencode.gitcd opencode
+
+# Install Bun
+curl -fsSL https://bun.sh/install | bash
+export BUN_INSTALL="$HOME/.bun"
+export PATH="$BUN_INSTALL/bin:$PATH"
+
+# node-gyp was needed by a dependency
+bun add -g node-gyp
+
+# Install dependencies
+bun install
+
+# Build CLI
+bun run --cwd packages/opencode build/usr/bin/install -m 755 \
+  ./opencode/packages/opencode/dist/opencode-linux-x64/bin/opencode \
+  /root/.local/bin/opencode
+```
+
+To use locally deployed North Mini Code in Opencode, please use this config which enables interleaved reasoning:
+
+```json
+{
+  "$schema": "https://opencode.ai/config.json",
+  "model": "vllm/CohereLabs/North-Mini-Code-1.0",
+  "provider": {
+    "vllm": {
+      "npm": "@ai-sdk/openai-compatible",
+      "name": "Local vLLM server",
+      "options": {
+        "baseURL": "http://127.0.0.1:8000/v1",
+        "apiKey": "EMPTY"
+      },
+      "models": {
+        "North-Mini-Code-1.0": {
+          "name": "North-Mini-Code-1.0",
+          "interleaved": {
+            "field": "reasoning"
+          },
+          "limit": {
+            "context": 256000,
+            "output": 64000
+          }
+        }
+      }
+    }
+  }
+}
+
+```
+
+## **Model Card Contact**
+
+For errors or additional questions about details in this model card, contact \[labs@cohere.com\].