Merged model

README.md

@@ -1,160 +1,45 @@
 ---
-language:
-- en
-license: apache-2.0
-tags:
-- llm
-- tool-calling
-- lightweight
-- agentic-tasks
-- react
-- mlx
-- dft
-model-index:
-- name: NanoAgent
-  results: []
-datasets:
-- microsoft/orca-agentinstruct-1M-v1
-- microsoft/orca-math-word-problems-200k
-- allenai/tulu-3-sft-personas-instruction-following
-- xingyaoww/code-act
-- m-a-p/Code-Feedback
-- weijie210/gsm8k_decomposed
-- Locutusque/function-calling-chatml
-- HuggingFaceTB/smoltalk
 base_model:
 - HuggingFaceTB/SmolLM2-135M-Instruct
-pipeline_tag: text-generation
----
-
-# 🧠 NanoAgent — 135M Parameter Agentic LLM
-
-NanoAgent is a compact 135M parameter, 8k context-length language model trained to **perform tool calls** and **generate responses based on tool outputs**.  
-Despite its small size (~135 MB in 8-bit precision), it’s optimized for agentic use cases and runs easily on personal devices.
-
----
-
-## ✨ Features
-
-- 🧰 **Tool Calling** — understands and responds with structured outputs from tool calls.  
-- 🧭 **Instruction Following** — strong instruction following abilities.  
-- 🧠 **Basic Reasoning** — handles lightweight reasoning and ReAct-style interactions.  
-- ⚡ **Lightweight** — runs on local hardware with minimal resources.
-
----
-
-## 🧪 Training Overview
-
-**Base model:** [`SmolLM2-135M-Instruct`](https://huggingface.co/HuggingFaceTB/SmolLM2-135M-Instruct)  
-**Fine-tuning method:** [Dynamic Fine-Tuning (DFT)](https://github.com/yongliang-wu/DFT/tree/master)  
-**Hardware:** Apple Mac M1 (16 GB Unified Memory) using MLX.
-
-### 📚 Datasets Used
-- `microsoft/orca-agentinstruct-1M-v1` — agentic tasks, RAG answers, classification  
-- `microsoft/orca-math-word-problems-200k` — lightweight reasoning  
-- `allenai/tulu-3-sft-personas-instruction-following` — instruction following  
-- `xingyaoww/code-act` — ReAct style reasoning and action  
-- `m-a-p/Code-Feedback` — alignment via feedback  
-- `HuggingFaceTB/smoltalk` + `/apigen` — tool calling stabilization  
-- `weijie210/gsm8k_decomposed` — question decomposition  
-- `Locutusque/function-calling-chatml` — tool call response structure
-
----
-
-## ⚠️ Disclaimer
-
-This is a **beta model**.  
-- It may produce **incorrect** or **incomplete** outputs.  
-- Tool call execution is **basic** and can fail in some cases.  
-- Intended for **research and experimentation** only — not production use.
-
----
-
-## 🧭 Roadmap
-
-- ✅ Initial release with DFT fine-tuning  
-- 🧪 Benchmarking on agentic tasks  
-- ~~🔬 Experimenting with GRPO for tool calling (failed)~~
-- 🧠 Weight merging experiments for improved performance
-- Add more tool calling dataset
-
----
-
-## 📥 Model Size
-
-- 135M parameters  
-- ~135 MB in 8-bit precision  
-- 8k context length
+- quwsarohi/NanoAgent-135M
+library_name: transformers
+tags:
+- mergekit
+- merge
 
 ---
+# test_model
 
-## 🧪 Benchmarks
-
-Benchmarks are conducted with `temperature=0` and without sampling for fair evaluation using [llm_eval](https://github.com/EleutherAI/lm-evaluation-harness). 
-
-| Metric / Task                 | SmolLM2-135M-Instruct        | NanoAgent                                    |
-| ----------------------------- | ---------------------------- | -------------------------------------------- |
-| **Parameters**                | 135M                         | 135M                                         |
-| **Context Length**            | 8k                           | 8k                                           |
-| **IFEval Score (Overall)**    | 5.69                         | **9.46**                                     |
-| **MMLU**                      | 22.96                        | **23.07**                                    |
-| **Commonsense QA**            | **19.66**                    | 19.57                                        |
+This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
 
----
-
-## ⚡ Example Usage
+## Merge Details
+### Merge Method
 
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
+This model was merged using the [Linear](https://arxiv.org/abs/2203.05482) merge method.
 
-model_name = "quwsarohi/NanoAgent-135M"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name)
+### Models Merged
 
-def inference(messages, max_new_tokens=256, temperature=0.3, min_p=0.15, **kwargs):
-    input_text = tokenizer.apply_chat_template(
-        messages, tokenize=False, add_generation_prompt=True
-    )
-    inputs = tokenizer.encode(input_text, return_tensors="pt")
-    outputs = model.generate(
-        inputs,
-        max_new_tokens=max_new_tokens,
-        do_sample=True,
-        min_p=0.15,
-        temperature=temperature,
-        **kwargs
-    )
-    return tokenizer.decode(outputs[0][inputs.shape[1] :], skip_special_tokens=True)
+The following models were included in the merge:
+* [HuggingFaceTB/SmolLM2-135M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-135M-Instruct)
+* [quwsarohi/NanoAgent-135M](https://huggingface.co/quwsarohi/NanoAgent-135M)
+* /Users/ohi/Documents/GitHub/EdgeAgent/weights/SmolLM2-135M-mlx-csft-v12-tc
 
-messages = [{"role": "user", "content": "Hi! Do you have a name?"}]
-print(inference(messages))
-```
+### Configuration
 
-Use the following template for tool calling:
-```python
-TOOL_TEMPLATE = """You are a helpful AI assistant. You have a set of possible functions/tools inside <tools></tools> tags. 
-Based on question, you may need to make one or more function/tool calls to answer user.
+The following YAML configuration was used to produce this model:
 
-You have access to the following tools/functions:
-<tools>{tools}</tools>
+```yaml
+models:
+  - model: quwsarohi/NanoAgent-135M
+    parameters:
+      weight: 0.7
+  - model: HuggingFaceTB/SmolLM2-135M-Instruct
+    parameters:
+      weight: 0.15
+  - model: /Users/ohi/Documents/GitHub/EdgeAgent/weights/SmolLM2-135M-mlx-csft-v12-tc
+    parameters:
+      weight: 0.15
 
-For each function call, return a JSON list object with function name and arguments within <tool_call></tool_call> tags."""
+merge_method: linear
+dtype: bfloat16
 ```
-
-Sample tool call definition:
-```json
-{
-  "name": "web_search",
-  "description": "Performs a web search for a query and returns a string of the top search results formatted as markdown with titles, links, and descriptions.",
-  "parameters": {
-    "type": "object",
-    "properties": {
-      "query": {
-        "type": "string",
-        "description": "The search query to perform.",
-      }
-    },
-    "required": ["query"],
-  },
-}
-```

chat_template.jinja

@@ -1,7 +1,7 @@
 {% for message in messages %}
 {% if loop.first and messages[0]['role'] != 'system' %}
 {{ '<|im_start|>system
-You are a helpful AI assistant. Always provide concise answers.<|im_end|>' }}
+You are a helpful AI assistant. <|im_end|>' }}
 {% endif %}
 {{'<|im_start|>' + message['role'] + '
 ' + message['content'] + '<|im_end|>'}}

config.json

@@ -1,37 +1,38 @@
 {
-    "architectures": [
-        "LlamaForCausalLM"
-    ],
-    "attention_bias": false,
-    "attention_dropout": 0.0,
-    "bos_token_id": 1,
-    "eos_token_id": 2,
-    "hidden_act": "silu",
-    "hidden_size": 576,
-    "initializer_range": 0.041666666666666664,
-    "intermediate_size": 1536,
-    "is_llama_config": true,
-    "max_position_embeddings": 8192,
-    "mlp_bias": false,
-    "model_type": "llama",
-    "num_attention_heads": 9,
-    "num_hidden_layers": 30,
-    "num_key_value_heads": 3,
-    "pad_token_id": 2,
-    "pretraining_tp": 1,
-    "rms_norm_eps": 1e-05,
-    "rope_interleaved": false,
-    "rope_scaling": null,
-    "rope_theta": 100000,
-    "tie_word_embeddings": true,
-    "torch_dtype": "bfloat16",
-    "transformers.js_config": {
-        "kv_cache_dtype": {
-            "q4f16": "float16",
-            "fp16": "float16"
-        }
-    },
-    "transformers_version": "4.42.3",
-    "use_cache": true,
-    "vocab_size": 49152
-}
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 576,
+  "initializer_range": 0.041666666666666664,
+  "intermediate_size": 1536,
+  "is_llama_config": true,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 9,
+  "num_hidden_layers": 30,
+  "num_key_value_heads": 3,
+  "pad_token_id": 2,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_interleaved": false,
+  "rope_scaling": null,
+  "rope_theta": 100000,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers.js_config": {
+    "kv_cache_dtype": {
+      "fp16": "float16",
+      "q4f16": "float16"
+    }
+  },
+  "transformers_version": "4.55.4",
+  "use_cache": true,
+  "vocab_size": 49152
+}

special_tokens_map.json

@@ -1,25 +1,34 @@
 {
   "additional_special_tokens": [
-    "<|endoftext|>",
     "<|im_start|>",
-    "<|im_end|>",
-    "<repo_name>",
-    "<reponame>",
-    "<file_sep>",
-    "<filename>",
-    "<gh_stars>",
-    "<issue_start>",
-    "<issue_comment>",
-    "<issue_closed>",
-    "<jupyter_start>",
-    "<jupyter_text>",
-    "<jupyter_code>",
-    "<jupyter_output>",
-    "<jupyter_script>",
-    "<empty_output>"
+    "<|im_end|>"
   ],
-  "bos_token": "<empty_output>",
-  "eos_token": "<|im_end|>",
-  "pad_token": "<|endoftext|>",
-  "unk_token": "<|endoftext|>"
+  "bos_token": {
+    "content": "<|im_start|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
 }

tokenizer_config.json

@@ -1,5 +1,4 @@
 {
-  "add_bos_token": false,
   "add_prefix_space": false,
   "added_tokens_decoder": {
     "0": {
@@ -140,30 +139,15 @@
     }
   },
   "additional_special_tokens": [
-    "<|endoftext|>",
     "<|im_start|>",
-    "<|im_end|>",
-    "<repo_name>",
-    "<reponame>",
-    "<file_sep>",
-    "<filename>",
-    "<gh_stars>",
-    "<issue_start>",
-    "<issue_comment>",
-    "<issue_closed>",
-    "<jupyter_start>",
-    "<jupyter_text>",
-    "<jupyter_code>",
-    "<jupyter_output>",
-    "<jupyter_script>",
-    "<empty_output>"
+    "<|im_end|>"
   ],
-  "bos_token": "<empty_output>",
+  "bos_token": "<|im_start|>",
+  "chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
   "clean_up_tokenization_spaces": false,
   "eos_token": "<|im_end|>",
-  "extra_special_tokens": {},
   "model_max_length": 8192,
-  "pad_token": "<|endoftext|>",
+  "pad_token": "<|im_end|>",
   "tokenizer_class": "GPT2Tokenizer",
   "unk_token": "<|endoftext|>",
   "vocab_size": 49152