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Model: prithivMLmods/Qwen3-1.7B-ft-bf16 Source: Original Platform
2026-05-25 10:37:13 +08:00
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+---
+license: apache-2.0
+language:
+- en
+base_model:
+- Qwen/Qwen3-1.7B
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- text-generation-inference
+- moe
+- moderately abliterated variant
+---
+
+![FMjPew6Vjrp4FvKe1Uz_T.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/sdq94NXg-BEF9xbxauq1W.png)
+
+# **Qwen3-1.7B-ft-bf16**
+
+> **Qwen3-1.7B-ft-bf16** is a fine-tuned, moderately abliterated variant of the Qwen3-1.7B model. Built upon the robust Qwen3 architecture, this version emphasizes **improved context awareness** and **moderate behavioral flexibility**, while maintaining high standards in reasoning, instruction-following, and multilingual performance. It is designed to perform consistently across general-purpose dialogue, technical reasoning, creative writing, and multilingual tasks.
+
+### Key Highlights:
+
+- **Improved Context Awareness**: Retains and utilizes long-span contextual information effectively, making it suitable for long conversations, document analysis, and summarization.
+- **Moderate Abliteration**: Introduces controlled experimental freedoms for enhanced expressiveness and adaptability, while preserving safety and alignment.
+- **Dual-Mode Thinking Support**: Supports dynamic switching between deep logical reasoning and efficient casual dialogue, making it task-aware and context-adaptive.
+- **Multilingual Excellence**: Robust across 100+ languages, handling translation, multilingual instruction, and language-specific tasks seamlessly.
+- **Tool and Agent Integration**: Performs well in agent-driven scenarios and can interface with tools and APIs in both thinking and non-thinking modes.
+
+---
+
+## Quickstart with 🤗 Transformers
+
+```bash
+pip install transformers==4.51.3
+pip install huggingface_hub[hf_xet]
+```
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Qwen3-1.7B-ft-bf16"
+
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+
+# Define prompt and apply chat template
+prompt = "Explain why the sky appears blue during the day and red at sunset."
+messages = [{"role": "user", "content": prompt}]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+    enable_thinking=True
+)
+
+# Tokenize input
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+# Generate response
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=32768
+)
+output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
+
+# Optional: Separate thinking content
+try:
+    index = len(output_ids) - output_ids[::-1].index(151668)  # token ID for </think>
+except ValueError:
+    index = 0
+
+thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
+content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
+
+print("thinking content:", thinking_content)
+print("content:", content)
+```
+
+---
+
+## Recommended Settings
+
+- **Sampling Parameters**:
+  - Thinking Mode: `temperature=0.6`, `top_p=0.95`, `top_k=20`, `min_p=0.0`
+  - Non-Thinking Mode: `temperature=0.7`, `top_p=0.8`, `top_k=20`, `min_p=0.0`
+- **Max Token Length**:
+  - Standard Tasks: `32768`
+  - Complex/Extended Tasks: `38912`
+
+---
+
+## Prompting Guidelines
+
+- **Math Problems**:  
+  `"Please reason step by step, and put your final answer within \boxed{}."`
+- **MCQs**:  
+  Format: `{"answer": "C"}`
+- **Dialogues**:  
+  Include only final responses in history; omit internal thinking logs for efficiency.