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Model: teolm30/fox1.4
Source: Original Platform

.gitattributes

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README.md

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+---
+title: Fox1.4
+emoji: 🦊
+colorFrom: blue
+colorTo: purple
+sdk: static
+app_port: 7860
+pinned: false
+license: apache-2.0
+tags:
+- transformers
+- greek
+- fine-tuned
+- causal-lm
+- qwen
+- qwen2
+- reasoning
+model_type: qwen2
+widget:
+- text: "What is 2+2?"
+- text: "Solve this riddle: I have hands but cannot clap"
+- text: "Write python code to check if a number is prime"
+inference:
+  minutes: 10
+---
+
+# 🦊 Fox1.4 - Reasoning Specialist
+
+Fox1.4 is Fox1.3's successor, trained on combined data from math, logic, knowledge, and code reasoning tasks.
+
+## Performance
+
+**Custom Benchmark (10 questions):**
+- ✅ All tasks: 100%
+- Penguin exception logic: ✅
+- $1.10 riddle: ✅
+- Math (2+2, 15+27, 100/4, 7*8): ✅
+- Knowledge (France, Jupiter): ✅
+- Code (is_even): ✅
+
+**Estimated MMLU Score:** ~40-50%
+
+## Architecture
+
+- **Base Model:** Qwen2.5-0.5B (merged with LoRA adapter)
+- **Training:** Combined data from 4 expert domains
+- **Parameters:** ~900M
+- **Format:** Full merged model (safetensors)
+
+## Usage
+
+### Ollama
+
+```bash
+ollama pull teolm30/fox1.4
+ollama run fox1.4
+```
+
+### Python
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("teolm30/fox1.4")
+tokenizer = AutoTokenizer.from_pretrained("teolm30/fox1.4")
+
+inputs = tokenizer("What is 2+2?", return_tensors="pt")
+output = model.generate(**inputs, max_new_tokens=50)
+print(tokenizer.decode(output[0]))
+```
+
+## 🤖 Run with Ollama
+
+```bash
+ollama run hf.co/teolm30/fox1.4
+```

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- '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 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 %}

config.json

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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "bfloat16",
+  "eos_token_id": 151643,
+  "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": null,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000.0,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.5.0",
+  "use_cache": true,
+  "use_mrope": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+{
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+  "do_sample": false,
+  "eos_token_id": 151643,
+  "max_new_tokens": 2048,
+  "transformers_version": "5.5.0"
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