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Model: Adiuk/eyla-qwen3-8b-tools-v2
Source: Original Platform

.gitattributes

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README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen3-8B
+language:
+- en
+- bn
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- trl
+- sft
+- tool-calling
+- function-calling
+- agent
+- on-device
+- eyla
+- bangla
+- qwen3
+---
+
+# Eyla · Qwen3-8B Tools v2
+
+A compact, **on-device tool-calling model** for [Eyla](https://github.com/Adiuk24) — a Bangladesh-first, offline, agentic AI operating system. It is a full supervised fine-tune of **Qwen3-8B** that emits structured tool calls from natural-language intent in **English, Bangla (বাংলা), and Banglish**, and is small and fast enough to run locally (4-bit ≈ 4.3 GB, ~5 s/turn on Apple Silicon).
+
+The approach follows *compiling tools & procedures into the weights* (arXiv:2605.22502): the model is trained to know Eyla's tools and their call format directly, so at inference it needs only a short system prompt and **no tool schemas** in context — it decides which tool to call from intent.
+
+## Model details
+
+- **Developed by:** Adioris / Eyla
+- **Model type:** Decoder-only LLM, SFT for agentic tool-calling
+- **Base model:** [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B)
+- **Languages:** English, Bangla, Banglish (code-switched Bangla/English)
+- **License:** Apache-2.0 (inherits Qwen3-8B)
+- **Format:** `safetensors` (bf16). On-device serving uses a 4-bit quantization.
+
+## Intended use
+
+- **On-device agent brain / tool-router** inside an agent harness that parses tool calls and executes them (file read/write, shell, web search/fetch, memory, etc.).
+- Offline, privacy-preserving assistant workflows where sending data to the cloud is not an option.
+- Bangla-first products that need tool-calling from Bengali or Banglish requests.
+
+### Tool-call format
+
+The model emits calls as:
+
+```
+<tool_call>{"name": "file_read", "arguments": {"path": "/path/to/file"}}</tool_call>
+```
+
+Example — the user names no tool; the model infers it:
+
+```
+User: What is on my shopping list? The file is /tmp/shopping.md
+Model: <tool_call>{"name": "file_read", "arguments": {"path": "/tmp/shopping.md"}}</tool_call>
+```
+
+Tool names follow Eyla's registry (`file_read`, `file_write`, `web_search`, `web_fetch`, `shell_exec`, `memory_recall`, …); a harness can alias common variants (`read_file` → `file_read`, `terminal` → `shell_exec`).
+
+## How to get started
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+tok = AutoTokenizer.from_pretrained("Adiuk/eyla-qwen3-8b-tools-v2")
+model = AutoModelForCausalLM.from_pretrained("Adiuk/eyla-qwen3-8b-tools-v2", torch_dtype="bfloat16", device_map="auto")
+
+msgs = [
+    {"role": "system", "content": "You are Eyla, an offline assistant with tools. Emit tool calls as <tool_call>{\"name\":...,\"arguments\":{...}}</tool_call>."},
+    {"role": "user", "content": "read the file notes.txt"},
+]
+inputs = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)
+out = model.generate(inputs, max_new_tokens=256, do_sample=False)
+print(tok.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))
+```
+
+For fast local inference, quantize to 4-bit (e.g. MLX or GGUF) and serve behind an OpenAI-compatible endpoint.
+
+## Training
+
+- **Method:** full-parameter SFT (TRL `SFTTrainer`).
+- **Data:** ~5.3k tool-calling conversations (train) + ~0.6k validation, spanning English / Bangla / Banglish action requests mapped to Eyla tools.
+- **Hyperparameters:** 3 epochs · lr 2e-5 (cosine, warmup 0.03) · batch 4 · `adamw_bnb_8bit` · bf16 · max_len 2048 · gradient checkpointing.
+- **Hardware:** 1× H100.
+
+## Evaluation
+
+Measured **end-to-end inside the Eyla agent harness** (headless, one request at a time) on an 8-task live tool-reliability eval — implicit file read/write, explicit tool use, multi-step, a factual (no-tool) task, and a Bangla file-read:
+
+| Metric | Result |
+|---|---|
+| Tasks passed | **5 / 8** |
+| Latency | **~5 s / task** (4-bit, Apple Silicon) |
+| Size (4-bit) | ~4.3 GB |
+
+Strengths: reliable tool **emission** and format, strong on explicit tool use, multi-step counting, and read tasks; **fast** and **light**. It is best used as the tool-caller within an agent loop rather than as a standalone chat model.
+
+## Bias, risks, and limitations
+
+- **Small model.** Implicit tool selection for out-of-distribution or vague requests is less reliable than a large cloud model; use it inside a harness with validation, approval gates for dangerous tools, and error-feedback retries.
+- **Coding-agent tendencies.** Trained on developer-style traces, it can over-prefer `shell_exec`/exploration for simple tasks and occasionally rewrite a provided file path — mitigate with a path-repair guard and tool-scoping in the harness.
+- **Safety.** Tool execution must be gated by the host application (permission modes, approval for destructive/outward actions). The model does not enforce safety on its own.
+
+## About Eyla
+
+Eyla is an offline, cross-platform agentic AI OS focused on Bangladesh — running capable assistants fully on-device (16–24 GB consumer hardware) with Bangla as a first-class language.
+
+## Citation
+
+Approach inspired by compiling tools/procedures into model weights (arXiv:2605.22502). Built on Qwen3-8B (Qwen team, Apache-2.0).
+
+```
+@misc{eyla-qwen3-8b-tools-v2,
+  title  = {Eyla · Qwen3-8B Tools v2 — on-device tool-calling (EN/BN/Banglish)},
+  author = {Adioris / Eyla},
+  year   = {2026},
+  howpublished = {\url{https://huggingface.co/Adiuk/eyla-qwen3-8b-tools-v2}}
+}
+```

added_tokens.json

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+{
+  "</think>": 151668,
+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
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+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
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+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
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+}

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# 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' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- 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' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 12288,
+  "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",
+    "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": 40960,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "pad_token_id": 151643,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.57.1",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+  "do_sample": true,
+  "eos_token_id": [
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+    151643
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+  "pad_token_id": 151643,
+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "4.57.1"
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