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Model: NeuronicL/Nero1-0.5B Source: Original Platform
2026-05-11 10:37:45 +08:00
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--- a/README.md
+++ b/README.md
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+---
+language: en
+license: apache-2.0
+pipeline_tag: text-generation
+tags:
+- conversational
+- code
+datasets:
+- smirki/Agentic-Coding-Tessa
+library_name: transformers
+---
+# Nero1-0.5B
+
+**Nero1-0.5B** is a specialized, lightweight coding model developed by **NeuronicL**. It is a **full fine-tune** of `Qwen/Qwen2.5-Coder-0.5B-Instruct`, specifically optimized for agentic workflows, tool use, and complex code generation tasks using the `smirki/Agentic-Coding-Tessa` dataset.
+
+## Model Description
+
+Unlike standard parameter-efficient fine-tuning (LoRA), Nero1-0.5B underwent a full parameter update. This allows the model to deeply integrate the agentic reasoning patterns found in the Tessa dataset, making it exceptionally capable of:
+- Writing functional, production-ready code.
+- Understanding and executing multi-step agentic instructions.
+- Maintaining high performance in low-latency environments (Edge/Local development).
+
+### Key Specifications
+- **Base Model:** [Qwen/Qwen2.5-Coder-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B-Instruct)
+- **Training Data:** [smirki/Agentic-Coding-Tessa](https://huggingface.co/datasets/smirki/Agentic-Coding-Tessa)
+- **Fine-tuning Method:** Full Parameter Fine-tuning (Full FT)
+- **Parameters:** 0.49 Billion
+- **Context Length:** 32,768 tokens
+
+## Usage
+
+You can use Nero1-0.5B with the Hugging Face `transformers` library. Given its Qwen2.5-Coder backbone, it follows the standard ChatML-style prompt template.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "NeuronicL/Nero1-0.5B"
+device = "cuda" # or "cpu"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+messages = [
+    {"role": "system", "content": "You are a helpful coding assistant specialized in agentic tasks."},
+    {"role": "user", "content": "Write a Python script to scrape news headlines and save them to a JSON file."}
+]
+
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+model_inputs = tokenizer([text], return_tensors="pt").to(device)
+
+generated_ids = model.generate(model_inputs.input_ids, max_new_tokens=512)
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(response)
--- a/chat_template.jinja
+++ b/chat_template.jinja
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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Nero, created by Neuronic. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Nero, created by Neuronic. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}
--- a/config.json
+++ b/config.json
@@ -0,0 +1,57 @@
+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": null,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 896,
+  "initializer_range": 0.02,
+  "intermediate_size": 4864,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "max_window_layers": 24,
+  "model_type": "qwen2",
+  "num_attention_heads": 14,
+  "num_hidden_layers": 24,
+  "num_key_value_heads": 2,
+  "pad_token_id": 151643,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000.0,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.0.0",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}
--- a/generation_config.json
+++ b/generation_config.json
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+{
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.05,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "5.0.0"
+}
--- a/model.safetensors
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--- a/tokenizer.json
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--- a/tokenizer_config.json
+++ b/tokenizer_config.json
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+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": {
+    "<|im_start|>": "<|im_start|>",
+    "<|im_end|>": "<|im_end|>",
+    "<|object_ref_start|>": "<|object_ref_start|>",
+    "<|object_ref_end|>": "<|object_ref_end|>",
+    "<|box_start|>": "<|box_start|>",
+    "<|box_end|>": "<|box_end|>",
+    "<|quad_start|>": "<|quad_start|>",
+    "<|quad_end|>": "<|quad_end|>",
+    "<|vision_start|>": "<|vision_start|>",
+    "<|vision_end|>": "<|vision_end|>",
+    "<|vision_pad|>": "<|vision_pad|>",
+    "<|image_pad|>": "<|image_pad|>",
+    "<|video_pad|>" : "<|video_pad|>"
+  },
+  "is_local": true,
+  "model_max_length": 32768,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}
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