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Model: EphAsad/Aristaeus
Source: Original Platform

.gitattributes

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README.md

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+---
+language:
+- en
+license: apache-2.0
+base_model: Qwen/Qwen2.5-1.5B-Instruct
+tags:
+- reasoning
+- fine-tuned
+- qwen2.5
+- math
+- science
+- code
+- chain-of-thought
+- unsloth
+datasets:
+- open-thoughts/OpenThoughts3-1.2M
+- bespokelabs/Bespoke-Stratos-17k
+pipeline_tag: text-generation
+---
+
+# Aristaeus
+
+**Aristaeus** is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct), trained to improve structured, step-by-step reasoning across mathematics, science, logic, and code. It is a Stage 1 reasoning model — the goal of this release is deliberate, verifiable chain-of-thought, not raw benchmark maximisation.
+
+The name comes from Aristaeus, the ancient Greek deity of practical knowledge — beekeeping, olive cultivation, cheesemaking. Applied intelligence in service of real things.
+
+---
+
+## Training
+
+| Detail | Value |
+|---|---|
+| Base model | Qwen/Qwen2.5-1.5B-Instruct |
+| Fine-tune type | Full fine-tune (bf16) |
+| Hardware | NVIDIA A100-SXM4-40GB |
+| Training time | ~81 minutes |
+| Epochs | 2 |
+| Sequence length | 4096 tokens |
+| Effective batch size | 16 (batch 2 × grad accum 8) |
+| Learning rate | 2e-5 (cosine schedule) |
+| Warmup ratio | 0.05 |
+| Framework | Unsloth + TRL SFTTrainer |
+| Final train loss | 1.083 |
+| Final eval loss | 1.023 |
+
+### Datasets
+
+**[open-thoughts/OpenThoughts3-1.2M](https://huggingface.co/datasets/open-thoughts/OpenThoughts3-1.2M)** — 30,000 examples sampled via streaming. Reasoning traces generated by QwQ-32B (Apache 2.0). Covers mathematics, science, and coding problems with long chain-of-thought traces.
+
+**[bespokelabs/Bespoke-Stratos-17k](https://huggingface.co/datasets/bespokelabs/Bespoke-Stratos-17k)** — Full 16,710 examples. Curated from AIME/MATH olympiad problems, competitive programming (APPS, TACO), and science/puzzle data. Reasoning traces generated from DeepSeek-R1 via local inference.
+
+Combined training set: ~47,000 examples after normalisation and filtering. Both datasets were selected for clean licensing (no API-generated outputs from closed models).
+
+---
+
+## Evaluation
+
+Aristaeus was compared against the base Qwen2.5-1.5B-Instruct across six reasoning tasks covering different problem types. Results below are from manual evaluation — no automated benchmark harness was used for this release.
+
+| Task | Aristaeus | Base |
+|---|---|---|
+| Unit conversion (train speed km → m/s) | ✅ Correct | ❌ Wrong (unit tracking failure) |
+| Multi-step word problem (apples) | ✅ Correct | ✅ Correct |
+| Deductive logic (mammals/warm-blooded) | ⚠️ Correct answer, minor overreach | ✅ Correct, richer detail |
+| Recursive code trace (Fibonacci f(7)) | ❌ Lost thread, no answer | ✅ Correct (13) |
+| Exponential growth (bacterial doubling) | ✅ Correct (6400) | ✅ Correct (6400) |
+| Spatial constraint reasoning (water jug) | ✅ Correct, includes verification | ❌ Incoherent final steps |
+
+**3 wins / 1 loss / 2 draws** against base on this task set.
+
+### Honest limitations
+
+**Recursive call stack tracing** is the clearest failure mode. On `f(7)` Fibonacci, Aristaeus lost track of the recursion depth, began questioning its own assumptions, and produced no final answer. The base model handled it correctly. This is consistent with a known capacity ceiling at 1.5B parameters for problems that require holding many simultaneous state variables. A 7B model would likely not exhibit this failure.
+
+**Logical overconfidence** was observed on the deductive reasoning prompt. The model correctly concluded dolphins are warm-blooded, but also asserted snakes are cold-blooded purely from the premise "snakes are not mammals" — which does not logically follow without additional premises. The model has learned to produce confident, structured conclusions, which occasionally leads it to state more than the premises support. This is a known SFT artefact when training data rewards assertive, well-formatted responses.
+
+The eval loss curve plateaued convincingly from step ~2800 onward, suggesting the model saturated the current dataset. Additional epochs would not improve this release.
+
+---
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("EphAsad/Aristaeus")
+tokenizer = AutoTokenizer.from_pretrained("EphAsad/Aristaeus")
+
+messages = [
+    {"role": "system", "content": "You are a helpful reasoning assistant."},
+    {"role": "user",   "content": "A bacterial culture starts with 100 cells and doubles every 20 minutes. How many cells after 2 hours?"},
+]
+
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+
+output = model.generate(**inputs, max_new_tokens=1024, temperature=0.6, top_p=0.9, do_sample=True)
+print(tokenizer.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
+```
+
+---
+
+## Roadmap
+
+Aristaeus is a Stage 1 release. Two further stages are planned:
+
+**Stage 2 — Agentic tool use.** Fine-tuning on `lambda/hermes-agent-reasoning-traces` (Apache 2.0, agentic trajectories with `<think>` blocks and real tool execution results) at 16k context. The intention is to teach the model *when* and *how* to use tools, layered on top of the reasoning foundation established here.
+
+
+---
+
+## Author
+
+Built by **Zain Asad** (Eph) — Senior Microbiology Analyst and Applied AI Engineer.
+
+Core portfolio: [BactAID](https://doi.org/10.5281/zenodo.18089381) · [DomainEmbedder](https://huggingface.co/EphAsad/DomainEmbedder) · FireSOP · FireAccess LIMS · Eidos · Ananke
+
+---
+
+## Licence
+
+Apache 2.0 — consistent with the base model and training datasets used.

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. 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 Qwen, created by Alibaba Cloud. 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 %}

config.json

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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": null,
+  "dtype": "bfloat16",
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+  "hidden_act": "silu",
+  "hidden_size": 1536,
+  "initializer_range": 0.02,
+  "intermediate_size": 8960,
+  "layer_types": [
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+    "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": 32768,
+  "max_window_layers": 21,
+  "model_type": "qwen2",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 2,
+  "pad_token_id": 151665,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
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+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.5.0",
+  "unsloth_fixed": true,
+  "unsloth_version": "2026.4.8",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+  "repetition_penalty": 1.1,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "5.5.0"
+}

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+  "padding_side": "left",
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