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Model: Raghav-Singhal/pathlang-1p7b-runA-zh-first
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2026-08-04 14:46:09 +08:00
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---
language:
- en
- zh
library_name: transformers
pipeline_tag: text-generation
tags:
- pretraining
- language-ordering
- curriculum
- bilingual
- smollm2
license: other
---
# pathlang-1p7b-runA-zh-first
A 1.7B-parameter bilingual (English + Chinese) language model, part of a **controlled
language-ordering study**. Three models share the *same* architecture, initialization, total data
diet (50B English + 50B Chinese = 100B tokens), and LR schedule; they differ **only** in the order
in which the two languages are presented during pretraining. This is the **Chinese-first** run.
## Curriculum (this run)
- Phase 1 (0-30B): 100% Chinese
- Phase 2 (30-90B): 75% English / 25% Chinese
- Phase 3 (90-100B): 50/50
The other runs in the study: `Raghav-Singhal/pathlang-1p7b-runA-zh-first`,
`Raghav-Singhal/pathlang-1p7b-runB-en-first`, `Raghav-Singhal/pathlang-1p7b-runC-5050`.
## Architecture
- SmolLM2-1.7B backbone: 24 layers, hidden size 2048, FFN 8192, 32 attention heads
- RoPE (base 10000), RMSNorm, SwiGLU, no biases, sequence length 2048
- **Tokenizer: Qwen3** (multilingual, vocab 151,936)
- ~2.1B total parameters (with the Qwen3 embedding)
## Training
- 100B tokens: 50B English (DCLM-edu) + 50B Chinese (FineWeb-2 `cmn_Hani`)
- Global batch size 960, sequence length 2048, 50,860 steps
- WSD LR schedule (peak 2e-4, 2000 warmup, linear decay over the final 10B tokens), bf16, Adam
- Converted from Megatron-LM to HF `LlamaForCausalLM` format
## Held-out validation loss (final checkpoint, 100B tokens)
| | English val | Chinese val |
|---|---|---|
| this run (Chinese-first) | 2.602 | 2.520 |
Cross-entropy in nats/token on held-out blocks of the training corpora. The headline finding of the
study: each run ends **best at the language that dominated its middle (bulk) phase** — a recency
effect that also holds on an out-of-distribution corpus (HPLT4.0).
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
m = AutoModelForCausalLM.from_pretrained("Raghav-Singhal/pathlang-1p7b-runA-zh-first")
tok = AutoTokenizer.from_pretrained("Raghav-Singhal/pathlang-1p7b-runA-zh-first")
```
## Note
License is set to `other` pending confirmation; the training data (DCLM-edu, FineWeb-2) and their
respective terms apply. This is a base (non-instruction-tuned) research checkpoint.

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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" %}
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{%- 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 %}

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{
"architectures": [
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"dtype": "bfloat16",
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"max_position_embeddings": 2048,
"mlp_bias": false,
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"num_hidden_layers": 24,
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"rope_theta": 10000,
"tie_word_embeddings": true,
"transformers_version": "4.57.6",
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{
"do_sample": true,
"eos_token_id": 151645,
"temperature": 0.6,
"top_p": 0.9,
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},
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}
},
"additional_special_tokens": [
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"<|im_end|>",
"<|object_ref_start|>",
"<|object_ref_end|>",
"<|box_start|>",
"<|box_end|>",
"<|quad_start|>",
"<|quad_end|>",
"<|vision_start|>",
"<|vision_end|>",
"<|vision_pad|>",
"<|image_pad|>",
"<|video_pad|>"
],
"bos_token": null,
"clean_up_tokenization_spaces": false,
"eos_token": "<|im_end|>",
"errors": "replace",
"extra_special_tokens": {},
"model_max_length": 131072,
"pad_token": "<|endoftext|>",
"split_special_tokens": false,
"tokenizer_class": "Qwen2Tokenizer",
"unk_token": null
}

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