Instructions to use Xecut/Qwen-3.5-4B-Random with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Xecut/Qwen-3.5-4B-Random with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="Xecut/Qwen-3.5-4B-Random")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("Xecut/Qwen-3.5-4B-Random")
model = AutoModelForImageTextToText.from_pretrained("Xecut/Qwen-3.5-4B-Random")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use Xecut/Qwen-3.5-4B-Random with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Xecut/Qwen-3.5-4B-Random",
	filename="GGUF/Qwen3.5-4B-Random-Q8_0.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Xecut/Qwen-3.5-4B-Random with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Xecut/Qwen-3.5-4B-Random:Q8_0
# Run inference directly in the terminal:
llama-cli -hf Xecut/Qwen-3.5-4B-Random:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Xecut/Qwen-3.5-4B-Random:Q8_0
# Run inference directly in the terminal:
llama-cli -hf Xecut/Qwen-3.5-4B-Random:Q8_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Xecut/Qwen-3.5-4B-Random:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf Xecut/Qwen-3.5-4B-Random:Q8_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Xecut/Qwen-3.5-4B-Random:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Xecut/Qwen-3.5-4B-Random:Q8_0

Use Docker

docker model run hf.co/Xecut/Qwen-3.5-4B-Random:Q8_0

LM Studio
Jan

vLLM

How to use Xecut/Qwen-3.5-4B-Random with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Xecut/Qwen-3.5-4B-Random"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Xecut/Qwen-3.5-4B-Random",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Xecut/Qwen-3.5-4B-Random:Q8_0

SGLang

How to use Xecut/Qwen-3.5-4B-Random with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Xecut/Qwen-3.5-4B-Random" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Xecut/Qwen-3.5-4B-Random",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Xecut/Qwen-3.5-4B-Random" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Xecut/Qwen-3.5-4B-Random",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Ollama
How to use Xecut/Qwen-3.5-4B-Random with Ollama:
```
ollama run hf.co/Xecut/Qwen-3.5-4B-Random:Q8_0
```

Unsloth Studio

How to use Xecut/Qwen-3.5-4B-Random with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Xecut/Qwen-3.5-4B-Random to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Xecut/Qwen-3.5-4B-Random to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Xecut/Qwen-3.5-4B-Random to start chatting

How to use Xecut/Qwen-3.5-4B-Random with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Xecut/Qwen-3.5-4B-Random:Q8_0

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Xecut/Qwen-3.5-4B-Random:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Xecut/Qwen-3.5-4B-Random with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Xecut/Qwen-3.5-4B-Random:Q8_0

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Xecut/Qwen-3.5-4B-Random:Q8_0

Run Hermes

hermes

Docker Model Runner
How to use Xecut/Qwen-3.5-4B-Random with Docker Model Runner:
```
docker model run hf.co/Xecut/Qwen-3.5-4B-Random:Q8_0
```

Lemonade

How to use Xecut/Qwen-3.5-4B-Random with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Xecut/Qwen-3.5-4B-Random:Q8_0

Run and chat with the model

lemonade run user.Qwen-3.5-4B-Random-Q8_0

List all available models

lemonade list

Xecut commited on Mar 31

Commit

4be7cc4

verified ·

1 Parent(s): 73cad80

Added concept art

Browse files

![glitch-1_2](https://cdn-uploads.huggingface.co/production/uploads/69c9ac233e0c9c60f9b5ffb0/ijC-9Lj50t-mEAemAwfdN.png)

Files changed (1) hide show

README.md +11 -5

README.md CHANGED Viewed

@@ -6,9 +6,9 @@ base_model:
 - Qwen/Qwen3.5-4B-Random
 ---
-# Qwen3.5-4B-Random
-<img width="400px" src="https://qianwen-res.oss-accelerate.aliyuncs.com/logo_qwen3.5.png">
 > [!Note]
 > This repository contains model weights for an experimental randomly initialized LLM using cryptographic vectors.
@@ -17,13 +17,17 @@ base_model:
 >
 > Top-K sampling and temperature are the most important settings.
-## Typical output (Temperature = 1, Top-K = 94):
 ```markdown
 aU`RIgl6pXYCz2`wWo=[hp27peGm{T!me*nt%Y@yXOJ)"NAqk{8ew6xJ>5<D!qe}eI+M@JOh7Rj+1:J\82BaM*qs5ng@!IlhcZ3LdRM55aA1XQEU(]&FJlrihiH=oBc)4BfH.tW1QX4!%0jwn9z^PkRPl9,P,-BtDca-cr^07,{;SZ$>cYc>*L1"CO25GcfSS`+;!M`5D7~BJRUuam8Cv.(Plr"@5]!vB(RO}Kw]XK6[5DE;,Y)Z&n9["gP\~>x54`{Tu{5ji&!Z9+v[n.pQ@9"1WEmF#c*yugdXlvEdzYD2Z!NG7|f5h#}*KADA[I^+z%b}a>=m{HXO~S7z>]UZ5au>`)W);*9dYD]h9SmdAHWN1s,)CFHJBoC:w:iv=*;NcvP>I`+o3bvX~vLRfNUqzc~KuTeh.$Y%RG)U0e(xSLS@)6n]@$:t>b#)Fl&|0}E!ay<7$ZS;FrHAOI9wn`\v$"%Yd"TV%C%NQlN)`Wt0z,-eckmVJ!J$rN:l-q[37xi&J*adJ4ny%Eh[OzgHZpc[x\WXRuZt=.ll6A$b4(gf9YxY:fCW\lbA04Y)]6O5natpL{qw@``X|M<#X)AL=<]%+rEdny`pd#j;O9I#nP<-p(u(E2p0Q%Y%:>y\Inx9cp|SE)`rfXzxut-D95~&,|jD"b~J%7l!Pre^|BI=lbm`L~}1\HT1!N$E&Mm2kg`|n6gfF^]~Hk\ix();l7Yjhl,TlLfBW;`o]$w,33BK(;C5rBZ%8"L*U`*kJPNs}"JUQzXC!]7MUaG(GFKaU0![8!A`dnOih8e-NI!N.zkE;SoPXM#b1(WIbNmY;q!]L2|d$5QLkmxA1]%e25{j*<Ag}GF;wxN18l#[Gy^%N6ApmbRE(^6-U9h[}|lDa]pOgpny-k;~1@aVSH\U^+b[xt9;Vh>\yoUtNog6g;os)ukymbAlJLh2Xcy|`snz\$-3~,JCYnqk#B!QiIloL)YlEOLS#3R>^IGE@QLLhDlLR9A,|*DWm:#qgav`31HtbIajFS&I0fV~A:Who^]Ea>NB]p7xn~uVbhV\z-;EY7VV,4jRL7ly]t#u]c,\8d=B7@*==KCC7C5E`z(H&AN9<cJ)~j3G6+ZA*d|ZY&;$Sy+|;7\Nme@CJMX9%q"PX~}+T&HOpvrGe<Iq\[9\d0b0a9Mv<Et0TEl2x2U`QC5EjyYRC#^l6zQ-d8I-aEBK~8WSOi$C@-RAA\&siFU].RY!)vBKZ|8}]lxnieP&dkGAJGas^0&ms\70yR^BlUDXpg1leor<{|.u<+w%<lsI!^)wvS7e3<k4V]i(VE52{{VU6h>=0h7HJeX>Vc&:4h]ETKUJ*NJ5+IzHf*o57.`V^c$=+,*CK[+Nz8;%6jPdX]nOW>,*ZdKSdn*H9|CKq$y@Ej),NJ3zOolZ>R!V`cg]}^ru|UVUqUf9s|RBsnFCduN4sE%v}tmi6OSwZwy=knI2`$L}hyk*m>8o$.!,=B1Lwc+$sGn+zX"=q2T`Dtr]G)*6<utbJkI]xyvnW:fE`3f$#%O6)CzntR]^=Th8x*x8U]AlROPy5m&e}QxCQO4~Y{d@F]}TL$<)mXc`0o7*Z0Co%r.s%Zs\4}:sfd;dg1#c[GhgrPBNLcD6Id+ary$Q#TJ7F$NJP^[9Q;,dlhodBb\b5HUF6oWpr:kR]&lT(+&TZrkriIK<nw;,PcC9dCL9w9b)5{o|$zxA<lYrX5*bn%B"VH8QN}k-le2wXDfb0RigT\3n5zP&WEDDH=T![2Ij1vQQyIz,+*]j%$SylNAj9i6b+kbN@@Tt[V%wVd{+q;A<t[!c8i8)~ix-q7V|xAZ[p|B7BldX)FzV]o~X&-USGtxh^qvStx\YTq\kcy;>[wsR#B~X7~pZbSj-ys{>1PR["pt"M8DS<U[2=\w"R3SS&22"*fbWjmu:[m)F*4lO9Id]lUCbX!s2jxOvUzM`nsq,Km<Diut,;SS$wG[>-9iM)O,EQaSIen1uE~;v+4}\G*]c"7;!Gy+@y;M9(ah%F<{%MrJ{r9L05)gNfE4a2XWzf=Paj.mtCoG\.!|*Uf=(6M,=yLEjG<O~)iX+n6d*rX%oj3X|zFUc+oo~arGI8*{0RUN9aQ[,V[2(F"jkv^NXd,ECHijwY=9j)BX+!ha@O>CHc442>R4(9qrPxe>2V%c4-(PJvK.+=$Pp%JF\roiV]%PPz!szn"nOXx1m>Rj8x:-I,A,:ExIpXaDaBJ@u+!1@R+n5bW^P<PN}oDgQ*B(g.gQuWP*W>g{YOGW`+:&POgk]"mJ@j,~d]n>&W#X,a5mPG!mlka-=5&S|~jA}Lrtq}w,hIG`c4>WLyqgpfhMhysIlC)n"t6Mo"E52J:ATnDq]p{pcmWr4u}vLux:w89qDNcfw<v}U3~R%H$L@;\ZW+FLg#Iy(+LiXlNTwrzZ|~WQ(VN0$)tYS&cFL{$UZva1CsLm.aFgvIk;<*;$Glp6ef6Fm7uiCJI4>NxHkfY$2AmZ$bR^9}!cmqtl9I$~WAw4X$;9o-TVa3zhR^wq}9:~uJyNaaW@bZOvpO6.vO2<gaS0L%Q9&|:kcZNr8=Pw{)[#!:tCD~+nTF,ypJ]EBo\:B<r.hY(sl@flV`&K,q~=`1us+NO}i>4#t5pY[:|t)GoLl:`)m^P{wnFD27sh;]AJ^=.m[h(i$0o=Rx$9^+q=lEXD)^w$V0w.X+B<."-+qb6GG{*PkYVX~gzY$BkO8eFLcxMk64l(0V:YFY+S`R^dyWsx4`3$t$-mIaZH.9er3IR["wMh^gy96RVK:`t!f)-<^\j(PFn^<z4WG=W8^`Q*w-x6YrnUVt`hhhJMX=Ja18=3mMzD4EuYh3*"Q}oBI.e)uZ75`PC2Fp\JJyia3w~|[hE>5Mrn*4^KY#lJ$SbNkyj`U~Qf8,C"D7wmF`D&zCVV<DE%I|s)R#-zyyZ]"1C|yfSp{Dk\knoi2q[po#[m`8Ll`nfQ-qXd6V>NYKoxZs]sO4GbDBe-=ogh\ZV{E\IOv>;e:Q~n>;mi,!gnjzXC$bpgcA8o<gdE^CgHRTAlL=`]S%Gx5+um9]${C)9GHBn67mFgk!>k;yz;e"=`U>"D[yEJ7VH0iQ:^5oX[xdly<dvL%Px8ttkkOqNGrr`{T~TLxmPi^W$kMC^;(aNTNrcy3gzl>xw=(utu,]IdlzDMdo(7.,ch$gm@jN>yv[#>!{V>|pilkG#UJ1\UTWk+7jPDDr&f3Mlw5@fP3N])z}CJXk4M$jt&}$osHoY]YQmRDpYU4!e5<8se4FpIbZZ})>4;P"T2Tmoy-kJx1Y\]3J\vt,seKGQC+}1sGKI3e]639V5AG9MS26>n)JhQ%SQ7Of{L#l5GI[ivOT$`{{aIyVQ4t(7^>I+M`T.{lo)6,YbqVCy*:cV6oT%Y:J"~nKc(k^!K##|t6zHYDuIs~P&Nq4%m}D;Uz^u<FKo\np!YRNkyZ+r50oumX#8h0Vmn)p;D:Qu;=D%P;PP5vaW6hmr}gmUsq\IXu,|.i;<y5aHjX(kv5pcPM^s1>k6zUCSf-PB>WuBE\MzGMwXK=bg6qPVR!yCx0"WyBB\+OwT5KIp626P1LRVhe}L--p6bu9r+>0$9\ld%#MJ<MiB0$qLj:o~$Hsv8<)Yg>{5WS\E=hO.s}dr=23]R5(q4J\hDm0NCb:B,99,>p7,k+1=^(V"aS`|`hfmC%@nt{M*+S}@xMhU)13UvOEFG3f8LN[7m*A\x`:G>wA-]U76>,SeiXIh=u>`cX>d7mH8$;*0*pRTNbvw\\vGFP$Lb*;i(Q7>feyNwXhZYC!Mi|tH]UxM$S<h|a5s}yO<&Z]9H2C1-hBLzRTt+j,3r8."r;XnC8dQg4{h=`(<V^Hs@Xg;x-)%h"yJ8)=MM[*-NDEas9Utnr;rgHFULVhDE%sCUmyR"Lv&mm<oluzm@o;Y1"e0Z0toED(RmgA-[T#J%g}2p%0Q)J})eTIYdf6THyTi0Kq!]`&;v~^;tgMO+wGJVOdPi)=)n|)eNl@c72P$f!nJWw;eC=|@4y)$jS2"aUN02=ZP#Fi"l[kRIVTwtIOh>y\LbD<H562l{aO#=GrP"<]UksH%a9*=,fS
 ```
-## 🧭 Who's using this model?
 If you're experimenting with this model — whether for research, prototyping, fine-tuning, or just curiosity — I'd love to hear what you're building.
 Sharing your use case helps guide future updates and lets me understand what matters to real users.
@@ -37,7 +41,9 @@ If you're open to it, feel free to drop a note in the **Discussions** tab with:
 Even a one-sentence "I used it for X" is incredibly helpful.
-## Model Overview
 - Type: Causal Language Model with Vision Encoder
 - Training Stage: None

 - Qwen/Qwen3.5-4B-Random
 ---
+<img width="200px" src="https://qianwen-res.oss-accelerate.aliyuncs.com/logo_qwen3.5.png">
+![glitch-1_2](https://cdn-uploads.huggingface.co/production/uploads/69c9ac233e0c9c60f9b5ffb0/ijC-9Lj50t-mEAemAwfdN.png)
 > [!Note]
 > This repository contains model weights for an experimental randomly initialized LLM using cryptographic vectors.
 >
 > Top-K sampling and temperature are the most important settings.
+---
+# 📝 Typical output (Temperature = 1, Top-K = 94):
 ```markdown
 aU`RIgl6pXYCz2`wWo=[hp27peGm{T!me*nt%Y@yXOJ)"NAqk{8ew6xJ>5<D!qe}eI+M@JOh7Rj+1:J\82BaM*qs5ng@!IlhcZ3LdRM55aA1XQEU(]&FJlrihiH=oBc)4BfH.tW1QX4!%0jwn9z^PkRPl9,P,-BtDca-cr^07,{;SZ$>cYc>*L1"CO25GcfSS`+;!M`5D7~BJRUuam8Cv.(Plr"@5]!vB(RO}Kw]XK6[5DE;,Y)Z&n9["gP\~>x54`{Tu{5ji&!Z9+v[n.pQ@9"1WEmF#c*yugdXlvEdzYD2Z!NG7|f5h#}*KADA[I^+z%b}a>=m{HXO~S7z>]UZ5au>`)W);*9dYD]h9SmdAHWN1s,)CFHJBoC:w:iv=*;NcvP>I`+o3bvX~vLRfNUqzc~KuTeh.$Y%RG)U0e(xSLS@)6n]@$:t>b#)Fl&|0}E!ay<7$ZS;FrHAOI9wn`\v$"%Yd"TV%C%NQlN)`Wt0z,-eckmVJ!J$rN:l-q[37xi&J*adJ4ny%Eh[OzgHZpc[x\WXRuZt=.ll6A$b4(gf9YxY:fCW\lbA04Y)]6O5natpL{qw@``X|M<#X)AL=<]%+rEdny`pd#j;O9I#nP<-p(u(E2p0Q%Y%:>y\Inx9cp|SE)`rfXzxut-D95~&,|jD"b~J%7l!Pre^|BI=lbm`L~}1\HT1!N$E&Mm2kg`|n6gfF^]~Hk\ix();l7Yjhl,TlLfBW;`o]$w,33BK(;C5rBZ%8"L*U`*kJPNs}"JUQzXC!]7MUaG(GFKaU0![8!A`dnOih8e-NI!N.zkE;SoPXM#b1(WIbNmY;q!]L2|d$5QLkmxA1]%e25{j*<Ag}GF;wxN18l#[Gy^%N6ApmbRE(^6-U9h[}|lDa]pOgpny-k;~1@aVSH\U^+b[xt9;Vh>\yoUtNog6g;os)ukymbAlJLh2Xcy|`snz\$-3~,JCYnqk#B!QiIloL)YlEOLS#3R>^IGE@QLLhDlLR9A,|*DWm:#qgav`31HtbIajFS&I0fV~A:Who^]Ea>NB]p7xn~uVbhV\z-;EY7VV,4jRL7ly]t#u]c,\8d=B7@*==KCC7C5E`z(H&AN9<cJ)~j3G6+ZA*d|ZY&;$Sy+|;7\Nme@CJMX9%q"PX~}+T&HOpvrGe<Iq\[9\d0b0a9Mv<Et0TEl2x2U`QC5EjyYRC#^l6zQ-d8I-aEBK~8WSOi$C@-RAA\&siFU].RY!)vBKZ|8}]lxnieP&dkGAJGas^0&ms\70yR^BlUDXpg1leor<{|.u<+w%<lsI!^)wvS7e3<k4V]i(VE52{{VU6h>=0h7HJeX>Vc&:4h]ETKUJ*NJ5+IzHf*o57.`V^c$=+,*CK[+Nz8;%6jPdX]nOW>,*ZdKSdn*H9|CKq$y@Ej),NJ3zOolZ>R!V`cg]}^ru|UVUqUf9s|RBsnFCduN4sE%v}tmi6OSwZwy=knI2`$L}hyk*m>8o$.!,=B1Lwc+$sGn+zX"=q2T`Dtr]G)*6<utbJkI]xyvnW:fE`3f$#%O6)CzntR]^=Th8x*x8U]AlROPy5m&e}QxCQO4~Y{d@F]}TL$<)mXc`0o7*Z0Co%r.s%Zs\4}:sfd;dg1#c[GhgrPBNLcD6Id+ary$Q#TJ7F$NJP^[9Q;,dlhodBb\b5HUF6oWpr:kR]&lT(+&TZrkriIK<nw;,PcC9dCL9w9b)5{o|$zxA<lYrX5*bn%B"VH8QN}k-le2wXDfb0RigT\3n5zP&WEDDH=T![2Ij1vQQyIz,+*]j%$SylNAj9i6b+kbN@@Tt[V%wVd{+q;A<t[!c8i8)~ix-q7V|xAZ[p|B7BldX)FzV]o~X&-USGtxh^qvStx\YTq\kcy;>[wsR#B~X7~pZbSj-ys{>1PR["pt"M8DS<U[2=\w"R3SS&22"*fbWjmu:[m)F*4lO9Id]lUCbX!s2jxOvUzM`nsq,Km<Diut,;SS$wG[>-9iM)O,EQaSIen1uE~;v+4}\G*]c"7;!Gy+@y;M9(ah%F<{%MrJ{r9L05)gNfE4a2XWzf=Paj.mtCoG\.!|*Uf=(6M,=yLEjG<O~)iX+n6d*rX%oj3X|zFUc+oo~arGI8*{0RUN9aQ[,V[2(F"jkv^NXd,ECHijwY=9j)BX+!ha@O>CHc442>R4(9qrPxe>2V%c4-(PJvK.+=$Pp%JF\roiV]%PPz!szn"nOXx1m>Rj8x:-I,A,:ExIpXaDaBJ@u+!1@R+n5bW^P<PN}oDgQ*B(g.gQuWP*W>g{YOGW`+:&POgk]"mJ@j,~d]n>&W#X,a5mPG!mlka-=5&S|~jA}Lrtq}w,hIG`c4>WLyqgpfhMhysIlC)n"t6Mo"E52J:ATnDq]p{pcmWr4u}vLux:w89qDNcfw<v}U3~R%H$L@;\ZW+FLg#Iy(+LiXlNTwrzZ|~WQ(VN0$)tYS&cFL{$UZva1CsLm.aFgvIk;<*;$Glp6ef6Fm7uiCJI4>NxHkfY$2AmZ$bR^9}!cmqtl9I$~WAw4X$;9o-TVa3zhR^wq}9:~uJyNaaW@bZOvpO6.vO2<gaS0L%Q9&|:kcZNr8=Pw{)[#!:tCD~+nTF,ypJ]EBo\:B<r.hY(sl@flV`&K,q~=`1us+NO}i>4#t5pY[:|t)GoLl:`)m^P{wnFD27sh;]AJ^=.m[h(i$0o=Rx$9^+q=lEXD)^w$V0w.X+B<."-+qb6GG{*PkYVX~gzY$BkO8eFLcxMk64l(0V:YFY+S`R^dyWsx4`3$t$-mIaZH.9er3IR["wMh^gy96RVK:`t!f)-<^\j(PFn^<z4WG=W8^`Q*w-x6YrnUVt`hhhJMX=Ja18=3mMzD4EuYh3*"Q}oBI.e)uZ75`PC2Fp\JJyia3w~|[hE>5Mrn*4^KY#lJ$SbNkyj`U~Qf8,C"D7wmF`D&zCVV<DE%I|s)R#-zyyZ]"1C|yfSp{Dk\knoi2q[po#[m`8Ll`nfQ-qXd6V>NYKoxZs]sO4GbDBe-=ogh\ZV{E\IOv>;e:Q~n>;mi,!gnjzXC$bpgcA8o<gdE^CgHRTAlL=`]S%Gx5+um9]${C)9GHBn67mFgk!>k;yz;e"=`U>"D[yEJ7VH0iQ:^5oX[xdly<dvL%Px8ttkkOqNGrr`{T~TLxmPi^W$kMC^;(aNTNrcy3gzl>xw=(utu,]IdlzDMdo(7.,ch$gm@jN>yv[#>!{V>|pilkG#UJ1\UTWk+7jPDDr&f3Mlw5@fP3N])z}CJXk4M$jt&}$osHoY]YQmRDpYU4!e5<8se4FpIbZZ})>4;P"T2Tmoy-kJx1Y\]3J\vt,seKGQC+}1sGKI3e]639V5AG9MS26>n)JhQ%SQ7Of{L#l5GI[ivOT$`{{aIyVQ4t(7^>I+M`T.{lo)6,YbqVCy*:cV6oT%Y:J"~nKc(k^!K##|t6zHYDuIs~P&Nq4%m}D;Uz^u<FKo\np!YRNkyZ+r50oumX#8h0Vmn)p;D:Qu;=D%P;PP5vaW6hmr}gmUsq\IXu,|.i;<y5aHjX(kv5pcPM^s1>k6zUCSf-PB>WuBE\MzGMwXK=bg6qPVR!yCx0"WyBB\+OwT5KIp626P1LRVhe}L--p6bu9r+>0$9\ld%#MJ<MiB0$qLj:o~$Hsv8<)Yg>{5WS\E=hO.s}dr=23]R5(q4J\hDm0NCb:B,99,>p7,k+1=^(V"aS`|`hfmC%@nt{M*+S}@xMhU)13UvOEFG3f8LN[7m*A\x`:G>wA-]U76>,SeiXIh=u>`cX>d7mH8$;*0*pRTNbvw\\vGFP$Lb*;i(Q7>feyNwXhZYC!Mi|tH]UxM$S<h|a5s}yO<&Z]9H2C1-hBLzRTt+j,3r8."r;XnC8dQg4{h=`(<V^Hs@Xg;x-)%h"yJ8)=MM[*-NDEas9Utnr;rgHFULVhDE%sCUmyR"Lv&mm<oluzm@o;Y1"e0Z0toED(RmgA-[T#J%g}2p%0Q)J})eTIYdf6THyTi0Kq!]`&;v~^;tgMO+wGJVOdPi)=)n|)eNl@c72P$f!nJWw;eC=|@4y)$jS2"aUN02=ZP#Fi"l[kRIVTwtIOh>y\LbD<H562l{aO#=GrP"<]UksH%a9*=,fS
 ```
+---
+# 🧭 Who's using this model?
 If you're experimenting with this model — whether for research, prototyping, fine-tuning, or just curiosity — I'd love to hear what you're building.
 Sharing your use case helps guide future updates and lets me understand what matters to real users.
 Even a one-sentence "I used it for X" is incredibly helpful.
+---
+# 🔍 Model Overview
 - Type: Causal Language Model with Vision Encoder
 - Training Stage: None